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Selective Search for Object Recognition


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This paper addresses the problem of generating possible object locations for use in object recognition. We introduce selective search which combines the strength of both an exhaustive search and segmentation. Like segmentation, we use the image structure to guide our sampling process. Like exhaustive search, we aim to capture all possible object locations. Instead of a single technique to generate possible object locations, we diversify our search and use a variety of complementary image partitionings to deal with as many image conditions as possible. Our selective search results in a small set of data-driven, class-independent, high quality locations, yielding 99 % recall and a Mean Average Best Overlap of 0.879 at 10,097 locations. The reduced number of locations compared to an exhaustive search enables the use of stronger machine learning techniques and stronger appearance models for object recognition. In this paper we show that our selective search enables the use of the powerful Bag-of-Words model for recognition. The selective search software is made publicly available (Software:
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Selective Search for Object Recognition
J.R.R. Uijlings1,2, K.E.A. van de Sande†2 , T. Gevers2, and A.W.M. Smeulders2
1University of Trento, Italy
2University of Amsterdam, the Netherlands
Technical Report 2012, submitted to IJCV
This paper addresses the problem of generating possible object lo-
cations for use in object recognition. We introduce Selective Search
which combines the strength of both an exhaustive search and seg-
mentation. Like segmentation, we use the image structure to guide
our sampling process. Like exhaustive search, we aim to capture
all possible object locations. Instead of a single technique to gen-
erate possible object locations, we diversify our search and use a
variety of complementary image partitionings to deal with as many
image conditions as possible. Our Selective Search results in a
small set of data-driven, class-independent, high quality locations,
yielding 99% recall and a Mean Average Best Overlap of 0.879 at
10,097 locations. The reduced number of locations compared to
an exhaustive search enables the use of stronger machine learning
techniques and stronger appearance models for object recognition.
In this paper we show that our selective search enables the use of
the powerful Bag-of-Words model for recognition. The Selective
Search software is made publicly available 1.
1 Introduction
For a long time, objects were sought to be delineated before their
identification. This gave rise to segmentation, which aims for
a unique partitioning of the image through a generic algorithm,
where there is one part for all object silhouettes in the image. Re-
search on this topic has yielded tremendous progress over the past
years [3, 6, 13, 26]. But images are intrinsically hierarchical: In
Figure 1a the salad and spoons are inside the salad bowl, which in
turn stands on the table. Furthermore, depending on the context the
term table in this picture can refer to only the wood or include ev-
erything on the table. Therefore both the nature of images and the
different uses of an object category are hierarchical. This prohibits
the unique partitioning of objects for all but the most specific pur-
poses. Hence for most tasks multiple scales in a segmentation are a
necessity. This is most naturally addressed by using a hierarchical
partitioning, as done for example by Arbelaez et al. [3].
Besides that a segmentation should be hierarchical, a generic so-
lution for segmentation using a single strategy may not exist at all.
There are many conflicting reasons why a region should be grouped
together: In Figure 1b the cats can be separated using colour, but
their texture is the same. Conversely, in Figure 1c the chameleon
(a) (b)
(c) (d)
Figure 1: There is a high variety of reasons that an image region
forms an object. In (b) the cats can be distinguished by colour, not
texture. In (c) the chameleon can be distinguished from the sur-
rounding leaves by texture, not colour. In (d) the wheels can be part
of the car because they are enclosed, not because they are similar
in texture or colour. Therefore, to find objects in a structured way
it is necessary to use a variety of diverse strategies. Furthermore,
an image is intrinsically hierarchical as there is no single scale for
which the complete table, salad bowl, and salad spoon can be found
in (a).
is similar to its surrounding leaves in terms of colour, yet its tex-
ture differs. Finally, in Figure 1d, the wheels are wildly different
from the car in terms of both colour and texture, yet are enclosed
by the car. Individual visual features therefore cannot resolve the
ambiguity of segmentation.
And, finally, there is a more fundamental problem. Regions with
very different characteristics, such as a face over a sweater, can
only be combined into one object after it has been established that
the object at hand is a human. Hence without prior recognition it is
hard to decide that a face and a sweater are part of one object [29].
This has led to the opposite of the traditional approach: to do
localisation through the identification of an object. This recent ap-
proach in object recognition has made enormous progress in less
than a decade [8, 12, 16, 35]. With an appearance model learned
from examples, an exhaustive search is performed where every lo-
cation within the image is examined as to not miss any potential
object location [8, 12, 16, 35].
However, the exhaustive search itself has several drawbacks.
Searching every possible location is computationally infeasible.
The search space has to be reduced by using a regular grid, fixed
scales, and fixed aspect ratios. In most cases the number of lo-
cations to visit remains huge, so much that alternative restrictions
need to be imposed. The classifier is simplified and the appearance
model needs to be fast. Furthermore, a uniform sampling yields
many boxes for which it is immediately clear that they are not sup-
portive of an object. Rather then sampling locations blindly using
an exhaustive search, a key question is: Can we steer the sampling
by a data-driven analysis?
In this paper, we aim to combine the best of the intuitions of seg-
mentation and exhaustive search and propose a data-driven selec-
tive search. Inspired by bottom-up segmentation, we aim to exploit
the structure of the image to generate object locations. Inspired by
exhaustive search, we aim to capture all possible object locations.
Therefore, instead of using a single sampling technique, we aim
to diversify the sampling techniques to account for as many image
conditions as possible. Specifically, we use a data-driven grouping-
based strategy where we increase diversity by using a variety of
complementary grouping criteria and a variety of complementary
colour spaces with different invariance properties. The set of lo-
cations is obtained by combining the locations of these comple-
mentary partitionings. Our goal is to generate a class-independent,
data-driven, selective search strategy that generates a small set of
high-quality object locations.
Our application domain of selective search is object recognition.
We therefore evaluate on the most commonly used dataset for this
purpose, the Pascal VOC detection challenge which consists of 20
object classes. The size of this dataset yields computational con-
straints for our selective search. Furthermore, the use of this dataset
means that the quality of locations is mainly evaluated in terms of
bounding boxes. However, our selective search applies to regions
as well and is also applicable to concepts such as “grass”.
In this paper we propose selective search for object recognition.
Our main research questions are: (1) What are good diversification
strategies for adapting segmentation as a selective search strategy?
(2) How effective is selective search in creating a small set of high-
quality locations within an image? (3) Can we use selective search
to employ more powerful classifiers and appearance models for ob-
ject recognition?
2 Related Work
We confine the related work to the domain of object recognition
and divide it into three categories: Exhaustive search, segmenta-
tion, and other sampling strategies that do not fall in either cate-
2.1 Exhaustive Search
As an object can be located at any position and scale in the image,
it is natural to search everywhere [8, 16, 36]. However, the visual
search space is huge, making an exhaustive search computationally
expensive. This imposes constraints on the evaluation cost per lo-
cation and/or the number of locations considered. Hence most of
these sliding window techniques use a coarse search grid and fixed
aspect ratios, using weak classifiers and economic image features
such as HOG [8, 16, 36]. This method is often used as a preselec-
tion step in a cascade of classifiers [16, 36].
Related to the sliding window technique is the highly successful
part-based object localisation method of Felzenszwalb et al. [12].
Their method also performs an exhaustive search using a linear
SVM and HOG features. However, they search for objects and
object parts, whose combination results in an impressive object de-
tection performance.
Lampert et al. [17] proposed using the appearance model to
guide the search. This both alleviates the constraints of using a
regular grid, fixed scales, and fixed aspect ratio, while at the same
time reduces the number of locations visited. This is done by di-
rectly searching for the optimal window within the image using a
branch and bound technique. While they obtain impressive results
for linear classifiers, [1] found that for non-linear classifiers the
method in practice still visits over a 100,000 windows per image.
Instead of a blind exhaustive search or a branch and bound
search, we propose selective search. We use the underlying im-
age structure to generate object locations. In contrast to the dis-
cussed methods, this yields a completely class-independent set of
locations. Furthermore, because we do not use a fixed aspect ra-
tio, our method is not limited to objects but should be able to find
stuff like “grass” and “sand” as well (this also holds for [17]). Fi-
nally, we hope to generate fewer locations, which should make the
problem easier as the variability of samples becomes lower. And
more importantly, it frees up computational power which can be
used for stronger machine learning techniques and more powerful
appearance models.
2.2 Segmentation
Both Carreira and Sminchisescu [4] and Endres and Hoiem [9] pro-
pose to generate a set of class independent object hypotheses using
segmentation. Both methods generate multiple foreground/back-
ground segmentations, learn to predict the likelihood that a fore-
ground segment is a complete object, and use this to rank the seg-
ments. Both algorithms show a promising ability to accurately
delineate objects within images, confirmed by [19] who achieve
state-of-the-art results on pixel-wise image classification using [4].
As common in segmentation, both methods rely on a single strong
algorithm for identifying good regions. They obtain a variety of
locations by using many randomly initialised foreground and back-
ground seeds. In contrast, we explicitly deal with a variety of image
conditions by using different grouping criteria and different repre-
sentations. This means a lower computational investment as we do
not have to invest in the single best segmentation strategy, such as
using the excellent yet expensive contour detector of [3]. Further-
more, as we deal with different image conditions separately, we
expect our locations to have a more consistent quality. Finally, our
selective search paradigm dictates that the most interesting ques-
tion is not how our regions compare to [4, 9], but rather how they
can complement each other.
Gu et al. [15] address the problem of carefully segmenting and
recognizing objects based on their parts. They first generate a set
of part hypotheses using a grouping method based on Arbelaez et
al. [3]. Each part hypothesis is described by both appearance and
shape features. Then, an object is recognized and carefully delin-
eated by using its parts, achieving good results for shape recogni-
tion. In their work, the segmentation is hierarchical and yields seg-
ments at all scales. However, they use a single grouping strategy
Figure 2: Two examples of our selective search showing the necessity of different scales. On the left we find many objects at different
scales. On the right we necessarily find the objects at different scales as the girl is contained by the tv.
whose power of discovering parts or objects is left unevaluated. In
this work, we use multiple complementary strategies to deal with
as many image conditions as possible. We include the locations
generated using [3] in our evaluation.
2.3 Other Sampling Strategies
Alexe et al. [2] address the problem of the large sampling space
of an exhaustive search by proposing to search for any object, in-
dependent of its class. In their method they train a classifier on the
object windows of those objects which have a well-defined shape
(as opposed to stuff like “grass” and “sand”). Then instead of a full
exhaustive search they randomly sample boxes to which they apply
their classifier. The boxes with the highest “objectness” measure
serve as a set of object hypotheses. This set is then used to greatly
reduce the number of windows evaluated by class-specific object
detectors. We compare our method with their work.
Another strategy is to use visual words of the Bag-of-Words
model to predict the object location. Vedaldi et al. [34] use jumping
windows [5], in which the relation between individual visual words
and the object location is learned to predict the object location in
new images. Maji and Malik [23] combine multiple of these rela-
tions to predict the object location using a Hough-transform, after
which they randomly sample windows close to the Hough maxi-
mum. In contrast to learning, we use the image structure to sample
a set of class-independent object hypotheses.
To summarize, our novelty is as follows. Instead of an exhaus-
tive search [8, 12, 16, 36] we use segmentation as selective search
yielding a small set of class independent object locations. In con-
trast to the segmentation of [4, 9], instead of focusing on the best
segmentation algorithm [3], we use a variety of strategies to deal
with as many image conditions as possible, thereby severely reduc-
ing computational costs while potentially capturing more objects
accurately. Instead of learning an objectness measure on randomly
sampled boxes [2], we use a bottom-up grouping procedure to gen-
erate good object locations.
3 Selective Search
In this section we detail our selective search algorithm for object
recognition and present a variety of diversification strategies to deal
with as many image conditions as possible. A selective search al-
gorithm is subject to the following design considerations:
Capture All Scales. Objects can occur at any scale within the im-
age. Furthermore, some objects have less clear boundaries
then other objects. Therefore, in selective search all object
scales have to be taken into account, as illustrated in Figure
2. This is most naturally achieved by using an hierarchical
Diversification. There is no single optimal strategy to group re-
gions together. As observed earlier in Figure 1, regions may
form an object because of only colour, only texture, or because
parts are enclosed. Furthermore, lighting conditions such as
shading and the colour of the light may influence how regions
form an object. Therefore instead of a single strategy which
works well in most cases, we want to have a diverse set of
strategies to deal with all cases.
Fast to Compute. The goal of selective search is to yield a set of
possible object locations for use in a practical object recogni-
tion framework. The creation of this set should not become a
computational bottleneck, hence our algorithm should be rea-
sonably fast.
3.1 Selective Search by Hierarchical Grouping
We take a hierarchical grouping algorithm to form the basis of our
selective search. Bottom-up grouping is a popular approach to seg-
mentation [6, 13], hence we adapt it for selective search. Because
the process of grouping itself is hierarchical, we can naturally gen-
erate locations at all scales by continuing the grouping process until
the whole image becomes a single region. This satisfies the condi-
tion of capturing all scales.
As regions can yield richer information than pixels, we want to
use region-based features whenever possible. To get a set of small
starting regions which ideally do not span multiple objects, we use
the fast method of Felzenszwalb and Huttenlocher [13], which [3]
found well-suited for such purpose.
Our grouping procedure now works as follows. We first use [13]
to create initial regions. Then we use a greedy algorithm to iter-
atively group regions together: First the similarities between all
neighbouring regions are calculated. The two most similar regions
are grouped together, and new similarities are calculated between
the resulting region and its neighbours. The process of grouping
the most similar regions is repeated until the whole image becomes
a single region. The general method is detailed in Algorithm 1.
Algorithm 1: Hierarchical Grouping Algorithm
Input: (colour) image
Output: Set of object location hypotheses L
Obtain initial regions R={r1,···,rn}using [13]
Initialise similarity set S=/0
foreach Neighbouring region pair (ri,rj)do
Calculate similarity s(ri,rj)
while S6=/0 do
Get highest similarity s(ri,rj) = max(S)
Merge corresponding regions rt=rirj
Remove similarities regarding ri:S=S\s(ri,r)
Remove similarities regarding rj:S=S\s(r,rj)
Calculate similarity set Stbetween rtand its neighbours
Extract object location boxes Lfrom all regions in R
For the similarity s(ri,rj)between region riand rjwe want a va-
riety of complementary measures under the constraint that they are
fast to compute. In effect, this means that the similarities should be
based on features that can be propagated through the hierarchy, i.e.
when merging region riand rjinto rt, the features of region rtneed
to be calculated from the features of riand rjwithout accessing the
image pixels.
3.2 Diversification Strategies
The second design criterion for selective search is to diversify the
sampling and create a set of complementary strategies whose loca-
tions are combined afterwards. We diversify our selective search
(1) by using a variety of colour spaces with different invariance
properties, (2) by using different similarity measures si j , and (3)
by varying our starting regions.
Complementary Colour Spaces. We want to account for dif-
ferent scene and lighting conditions. Therefore we perform our
hierarchical grouping algorithm in a variety of colour spaces with
a range of invariance properties. Specifically, we the following
colour spaces with an increasing degree of invariance: (1) RGB,
(2) the intensity (grey-scale image) I, (3) Lab, (4) the rg chan-
nels of normalized RGB plus intensity denoted as rgI, (5) H SV, (6)
normalized RGB denoted as rgb, (7) C[14] which is an opponent
colour space where intensity is divided out, and finally (8) the Hue
channel Hfrom HSV . The specific invariance properties are listed
in Table 1.
Of course, for images that are black and white a change of colour
space has little impact on the final outcome of the algorithm. For
colour channels R G B I V L a b S r g C H
Light Intensity - - - - - - +/- +/- + + + + +
Shadows/shading - - - - - - +/- +/- + + + + +
Highlights - - - - - - - - - - - +/- +
colour spaces RGB I Lab rgI HSV rgb C H
Light Intensity - - +/- 2
/3+ + +
Shadows/shading - - +/- 2
/3+ + +
Highlights - - - - 1
/3- +/- +
Table 1: The invariance properties of both the individual colour
channels and the colour spaces used in this paper, sorted by de-
gree of invariance. A “+/-” means partial invariance. A fraction
/3means that one of the three colour channels is invariant to said
these images we rely on the other diversification methods for en-
suring good object locations.
In this paper we always use a single colour space throughout
the algorithm, meaning that both the initial grouping algorithm of
[13] and our subsequent grouping algorithm are performed in this
colour space.
Complementary Similarity Measures. We define four comple-
mentary, fast-to-compute similarity measures. These measures are
all in range [0,1]which facilitates combinations of these measures.
scolour(ri,rj)measures colour similarity. Specifically, for each re-
gion we obtain one-dimensional colour histograms for each
colour channel using 25 bins, which we found to work well.
This leads to a colour histogram Ci={c1
i}for each
region riwith dimensionality n=75 when three colour chan-
nels are used. The colour histograms are normalised using the
L1norm. Similarity is measured using the histogram intersec-
scolour(ri,rj) =
The colour histograms can be efficiently propagated through
the hierarchy by
size(ri) + size(rj).(2)
The size of a resulting region is simply the sum of its con-
stituents: size(rt) = size(ri) + size(rj).
stexture(ri,rj)measures texture similarity. We represent texture us-
ing fast SIFT-like measurements as SIFT itself works well for
material recognition [20]. We take Gaussian derivatives in
eight orientations using
=1 for each colour channel. For
each orientation for each colour channel we extract a his-
togram using a bin size of 10. This leads to a texture his-
togram Ti={t1
i}for each region riwith dimension-
ality n=240 when three colour channels are used. Texture
histograms are normalised using the L1norm. Similarity is
measured using histogram intersection:
stexture(ri,rj) =
Texture histograms are efficiently propagated through the hi-
erarchy in the same way as the colour histograms.
ssize(ri,rj)encourages small regions to merge early. This forces
regions in S,i.e. regions which have not yet been merged, to
be of similar sizes throughout the algorithm. This is desir-
able because it ensures that object locations at all scales are
created at all parts of the image. For example, it prevents a
single region from gobbling up all other regions one by one,
yielding all scales only at the location of this growing region
and nowhere else. ssize(ri,rj)is defined as the fraction of the
image that riand rjjointly occupy:
ssize(ri,rj) = 1size(ri) + size(rj)
where size(im)denotes the size of the image in pixels.
sfill(ri,rj)measures how well region riand rjfit into each other.
The idea is to fill gaps: if riis contained in rjit is logical to
merge these first in order to avoid any holes. On the other
hand, if riand rjare hardly touching each other they will
likely form a strange region and should not be merged. To
keep the measure fast, we use only the size of the regions and
of the containing boxes. Specifically, we define BBi j to be the
tight bounding box around riand rj. Now sfill (ri,rj)is the
fraction of the image contained in BBi j which is not covered
by the regions of riand rj:
fill(ri,rj) = 1size(BBi j)size(ri)size(ri)
We divide by size(im)for consistency with Equation 4. Note
that this measure can be efficiently calculated by keeping track
of the bounding boxes around each region, as the bounding
box around two regions can be easily derived from these.
In this paper, our final similarity measure is a combination of the
above four:
s(ri,rj) = a1scolour (ri,rj) + a2stexture(ri,rj) +
a3ssize(ri,rj) + a4sf il l (ri,rj),(6)
where ai∈ {0,1}denotes if the similarity measure is used or
not. As we aim to diversify our strategies, we do not consider any
weighted similarities.
Complementary Starting Regions. A third diversification
strategy is varying the complementary starting regions. To the
best of our knowledge, the method of [13] is the fastest, publicly
available algorithm that yields high quality starting locations. We
could not find any other algorithm with similar computational effi-
ciency so we use only this oversegmentation in this paper. But note
that different starting regions are (already) obtained by varying the
colour spaces, each which has different invariance properties. Ad-
ditionally, we vary the threshold parameter kin [13].
3.3 Combining Locations
In this paper, we combine the object hypotheses of several varia-
tions of our hierarchical grouping algorithm. Ideally, we want to
order the object hypotheses in such a way that the locations which
are most likely to be an object come first. This enables one to find
a good trade-off between the quality and quantity of the resulting
object hypothesis set, depending on the computational efficiency of
the subsequent feature extraction and classification method.
We choose to order the combined object hypotheses set based
on the order in which the hypotheses were generated in each in-
dividual grouping strategy. However, as we combine results from
up to 80 different strategies, such order would too heavily empha-
size large regions. To prevent this, we include some randomness
as follows. Given a grouping strategy j, let rj
ibe the region which
is created at position iin the hierarchy, where i=1 represents the
top of the hierarchy (whose corresponding region covers the com-
plete image). We now calculate the position value vj
ias RND ×i,
where RND is a random number in range [0,1]. The final ranking
is obtained by ordering the regions using vj
When we use locations in terms of bounding boxes, we first rank
all the locations as detailed above. Only afterwards we filter out
lower ranked duplicates. This ensures that duplicate boxes have a
better chance of obtaining a high rank. This is desirable because
if multiple grouping strategies suggest the same box location, it is
likely to come from a visually coherent part of the image.
4 Object Recognition using Selective
This paper uses the locations generated by our selective search for
object recognition. This section details our framework for object
Two types of features are dominant in object recognition: his-
tograms of oriented gradients (HOG) [8] and bag-of-words [7, 27].
HOG has been shown to be successful in combination with the part-
based model by Felzenszwalb et al. [12]. However, as they use an
exhaustive search, HOG features in combination with a linear clas-
sifier is the only feasible choice from a computational perspective.
In contrast, our selective search enables the use of more expensive
and potentially more powerful features. Therefore we use bag-of-
words for object recognition [16, 17, 34]. However, we use a more
powerful (and expensive) implementation than [16, 17, 34] by em-
ploying a variety of colour-SIFT descriptors [32] and a finer spatial
pyramid division [18].
Specifically we sample descriptors at each pixel on a single scale
=1.2). Using software from [32], we extract SIFT [21] and two
colour SIFTs which were found to be the most sensitive for de-
tecting image structures, Extended OpponentSIFT [31] and RGB-
SIFT [32]. We use a visual codebook of size 4,000 and a spatial
pyramid with 4 levels using a 1x1, 2x2, 3x3. and 4x4 division.
This gives a total feature vector length of 360,000. In image clas-
sification, features of this size are already used [25, 37]. Because
a spatial pyramid results in a coarser spatial subdivision than the
cells which make up a HOG descriptor, our features contain less
information about the specific spatial layout of the object. There-
fore, HOG is better suited for rigid objects and our features are
better suited for deformable object types.
As classifier we employ a Support Vector Machine with a his-
togram intersection kernel using the Shogun Toolbox [28]. To ap-
ply the trained classifier, we use the fast, approximate classification
strategy of [22], which was shown to work well for Bag-of-Words
in [30].
Our training procedure is illustrated in Figure 3. The initial posi-
tive examples consist of all ground truth object windows. As initial
negative examples we select from all object locations generated
Figure 3: The training procedure of our object recognition pipeline. As positive learning examples we use the ground truth. As negatives
we use examples that have a 20-50% overlap with the positive examples. We iteratively add hard negatives using a retraining phase.
by our selective search that have an overlap of 20% to 50% with
a positive example. To avoid near-duplicate negative examples,
a negative example is excluded if it has more than 70% overlap
with another negative. To keep the number of initial negatives per
class below 20,000, we randomly drop half of the negatives for the
classes car,cat,dog and person. Intuitively, this set of examples
can be seen as difficult negatives which are close to the positive ex-
amples. This means they are close to the decision boundary and are
therefore likely to become support vectors even when the complete
set of negatives would be considered. Indeed, we found that this
selection of training examples gives reasonably good initial classi-
fication models.
Then we enter a retraining phase to iteratively add hard negative
examples (e.g. [12]): We apply the learned models to the training
set using the locations generated by our selective search. For each
negative image we add the highest scoring location. As our initial
training set already yields good models, our models converge in
only two iterations.
For the test set, the final model is applied to all locations gener-
ated by our selective search. The windows are sorted by classifier
score while windows which have more than 30% overlap with a
higher scoring window are considered near-duplicates and are re-
5 Evaluation
In this section we evaluate the quality of our selective search. We
divide our experiments in four parts, each spanning a separate sub-
Diversification Strategies. We experiment with a variety of
colour spaces, similarity measures, and thresholds of the ini-
tial regions, all which were detailed in Section 3.2. We seek a
trade-off between the number of generated object hypotheses,
computation time, and the quality of object locations. We do
this in terms of bounding boxes. This results in a selection of
complementary techniques which together serve as our final
selective search method.
Quality of Locations. We test the quality of the object location
hypotheses resulting from the selective search.
Object Recognition. We use the locations of our selective search
in the Object Recognition framework detailed in Section 4.
We evaluate performance on the Pascal VOC detection chal-
An upper bound of location quality. We investigate how well
our object recognition framework performs when using an ob-
ject hypothesis set of “perfect” quality. How does this com-
pare to the locations that our selective search generates?
To evaluate the quality of our object hypotheses we define
the Average Best Overlap (ABO) and Mean Average Best Over-
lap (MABO) scores, which slightly generalises the measure used
in [9]. To calculate the Average Best Overlap for a specific class c,
we calculate the best overlap between each ground truth annotation
iGcand the object hypotheses Lgenerated for the correspond-
ing image, and average:
ABO =1
The Overlap score is taken from [11] and measures the area of the
intersection of two regions divided by its union:
i,lj) = area(gc
Analogously to Average Precision and Mean Average Precision,
Mean Average Best Overlap is now defined as the mean ABO over
all classes.
Other work often uses the recall derived from the Pascal Overlap
Criterion to measure the quality of the boxes [1, 16, 34]. This crite-
rion considers an object to be found when the Overlap of Equation
8 is larger than 0.5. However, in many of our experiments we ob-
tain a recall between 95% and 100% for most classes, making this
measure too insensitive for this paper. However, we do report this
measure when comparing with other work.
To avoid overfitting, we perform the diversification strategies ex-
periments on the Pascal VOC 2007 TRA IN +VAL set. Other exper-
iments are done on the Pascal VOC 2007 TES T set. Additionally,
our object recognition system is benchmarked on the Pascal VOC
2010 detection challenge, using the independent evaluation server.
5.1 Diversification Strategies
In this section we evaluate a variety of strategies to obtain good
quality object location hypotheses using a reasonable number of
boxes computed within a reasonable amount of time.
5.1.1 Flat versus Hierarchy
In the description of our method we claim that using a full hierar-
chy is more natural than using multiple flat partitionings by chang-
ing a threshold. In this section we test whether the use of a hier-
archy also leads to better results. We therefore compare the use
of [13] with multiple thresholds against our proposed algorithm.
Specifically, we perform both strategies in RGB colour space. For
[13], we vary the threshold from k=50 to k=1000 in steps of 50.
This range captures both small and large regions. Additionally, as a
special type of threshold, we include the whole image as an object
location because quite a few images contain a single large object
only. Furthermore, we also take a coarser range from k=50 to
k=950 in steps of 100. For our algorithm, to create initial regions
we use a threshold of k=50, ensuring that both strategies have
an identical smallest scale. Additionally, as we generate fewer re-
gions, we combine results using k=50 and k=100. As similarity
measure Swe use the addition of all four similarities as defined in
Equation 6. Results are in table 2.
threshold kin [13] MABO # windows
Flat [13] k=50,150,···,950 0.659 387
Hierarchical (this paper) k=50 0.676 395
Flat [13] k=50,100,···,1000 0.673 597
Hierarchical (this paper) k=50,100 0.719 625
Table 2: A comparison of multiple flat partitionings against hier-
archical partitionings for generating box locations shows that for
the hierarchical strategy the Mean Average Best Overlap (MABO)
score is consistently higher at a similar number of locations.
As can be seen, the quality of object hypotheses is better for
our hierarchical strategy than for multiple flat partitionings: At a
similar number of regions, our MABO score is consistently higher.
Moreover, the increase in MABO achieved by combining the lo-
cations of two variants of our hierarchical grouping algorithm is
much higher than the increase achieved by adding extra thresholds
for the flat partitionings. We conclude that using all locations from
a hierarchical grouping algorithm is not only more natural but also
more effective than using multiple flat partitionings.
5.1.2 Individual Diversification Strategies
In this paper we propose three diversification strategies to obtain
good quality object hypotheses: varying the colour space, vary-
ing the similarity measures, and varying the thresholds to obtain
the starting regions. This section investigates the influence of each
strategy. As basic settings we use the RGB colour space, the com-
bination of all four similarity measures, and threshold k=50. Each
time we vary a single parameter. Results are given in Table 3.
We start examining the combination of similarity measures on
the left part of Table 3. Looking first at colour, texture, size, and fill
individually, we see that the texture similarity performs worst with
a MABO of 0.581, while the other measures range between 0.63
and 0.64. To test if the relatively low score of texture is due to our
choice of feature, we also tried to represent texture by Local Binary
Patterns [24]. We experimented with 4 and 8 neighbours on dif-
ferent scales using different uniformity/consistency of the patterns
(see [24]), where we concatenate LBP histograms of the individual
colour channels. However, we obtained similar results (MABO of
0.577). We believe that one reason of the weakness of texture is be-
cause of object boundaries: When two segments are separated by
an object boundary, both sides of this boundary will yield similar
edge-responses, which inadvertently increases similarity.
Similarities MABO # box Colours MABO # box
C 0.635 356 HSV 0.693 463
T 0.581 303 I 0.670 399
S 0.640 466 RGB 0.676 395
F 0.634 449 rgI 0.693 362
C+T 0.635 346 Lab 0.690 328
C+S 0.660 383 H 0.644 322
C+F 0.660 389 rgb 0.647 207
T+S 0.650 406 C 0.615 125
T+F 0.638 400 Thresholds MABO # box
S+F 0.638 449 50 0.676 395
C+T+S 0.662 377 100 0.671 239
C+T+F 0.659 381 150 0.668 168
C+S+F 0.674 401 250 0.647 102
T+S+F 0.655 427 500 0.585 46
C+T+S+F 0.676 395 1000 0.477 19
Table 3: Mean Average Best Overlap for box-based object hy-
potheses using a variety of segmentation strategies. (C)olour,
(S)ize, and (F)ill perform similar. (T)exture by itself is weak. The
best combination is as many diverse sources as possible.
While the texture similarity yields relatively few object loca-
tions, at 300 locations the other similarity measures still yield a
MABO higher than 0.628. This suggests that when comparing
individual strategies the final MABO scores in table 3 are good
indicators of trade-off between quality and quantity of the object
hypotheses. Another observation is that combinations of similarity
measures generally outperform the single measures. In fact, us-
ing all four similarity measures perform best yielding a MABO of
Looking at variations in the colour space in the top-right of Table
3, we observe large differences in results, ranging from a MABO
of 0.615 with 125 locations for the C colour space to a MABO of
0.693 with 463 locations for the HSV colour space. We note that
Lab-space has a particularly good MABO score of 0.690 using only
328 boxes. Furthermore, the order of each hierarchy is effective:
using the first 328 boxes of HSV colour space yields 0.690 MABO,
while using the first 100 boxes yields 0.647 MABO. This shows
that when comparing single strategies we can use only the MABO
scores to represent the trade-off between quality and quantity of
the object hypotheses set. We will use this in the next section when
finding good combinations.
Experiments on the thresholds of [13] to generate the starting
regions show, in the bottom-right of Table 3, that a lower initial
threshold results in a higher MABO using more object locations.
5.1.3 Combinations of Diversification Strategies
We combine object location hypotheses using a variety of com-
plementary grouping strategies in order to get a good quality set
of object locations. As a full search for the best combination is
computationally expensive, we perform a greedy search using the
MABO score only as optimization criterion. We have earlier ob-
served that this score is representative for the trade-off between the
number of locations and their quality.
From the resulting ordering we create three configurations: a
single best strategy, a fast selective search, and a quality selective
search using all combinations of individual components, i.e. colour
Version Strategies MABO # win # strategies time (s)
Single HSV
Strategy C+T+S+F 0.693 362 1 0.71
Selective HSV, Lab
Search C+T+S+F, T+S+F 0.799 2147 8 3.79
Fast k=50,100
Selective HSV, Lab, rgI, H, I
Search C+T+S+F, T+S+F, F, S 0.878 10,108 80 17.15
Quality k=50,100,150,300
Table 4: Our selective search methods resulting from a greedy
search. We take all combinations of the individual diversifica-
tion strategies selected, resulting in 1, 8, and 80 variants of our
hierarchical grouping algorithm. The Mean Average Best Over-
lap (MABO) score keeps steadily rising as the number of windows
method recall MABO # windows
Arbelaez et al. [3] 0.752 0.649 ±0.193 418
Alexe et al. [2] 0.944 0.694 ±0.111 1,853
Harzallah et al. [16] 0.830 - 200 per class
Carreira and Sminchisescu [4] 0.879 0.770 ±0.084 517
Endres and Hoiem [9] 0.912 0.791 ±0.082 790
Felzenszwalb et al. [12] 0.933 0.829 ±0.052 100,352 per class
Vedaldi et al. [34] 0.940 - 10,000 per class
Single Strategy 0.840 0.690 ±0.171 289
Selective search “Fast” 0.980 0.804 ±0.046 2,134
Selective search “Quality” 0.991 0.879 ±0.039 10,097
Table 5: Comparison of recall, Mean Average Best Overlap
(MABO) and number of window locations for a variety of meth-
ods on the Pascal 2007 TE ST set.
space, similarities, thresholds, as detailed in Table 4. The greedy
search emphasizes variation in the combination of similarity mea-
sures. This confirms our diversification hypothesis: In the quality
version, next to the combination of all similarities, Fill and Size
are taken separately. The remainder of this paper uses the three
strategies in Table 4.
5.2 Quality of Locations
In this section we evaluate our selective search algorithms in terms
of both Average Best Overlap and the number of locations on the
Pascal VOC 2007 TES T set. We first evaluate box-based locations
and afterwards briefly evaluate region-based locations.
5.2.1 Box-based Locations
We compare with the sliding window search of [16], the sliding
window search of [12] using the window ratio’s of their models,
the jumping windows of [34], the “objectness” boxes of [2], the
boxes around the hierarchical segmentation algorithm of [3], the
boxes around the regions of [9], and the boxes around the regions
of [4]. From these algorithms, only [3] is not designed for finding
object locations. Yet [3] is one of the best contour detectors pub-
licly available, and results in a natural hierarchy of regions. We
include it in our evaluation to see if this algorithm designed for
segmentation also performs well on finding good object locations.
Furthermore, [4, 9] are designed to find good object regions rather
then boxes. Results are shown in Table 5 and Figure 4.
As shown in Table 5, our “Fast” and “Quality” selective search
methods yield a close to optimal recall of 98% and 99% respec-
tively. In terms of MABO, we achieve 0.804 and 0.879 respec-
tively. To appreciate what a Best Overlap of 0.879 means, Figure
5 shows for bike, cow, and person an example location which has
an overlap score between 0.874 and 0.884. This illustrates that our
selective search yields high quality object locations.
Furthermore, note that the standard deviation of our MABO
scores is relatively low: 0.046 for the fast selective search, and
0.039 for the quality selective search. This shows that selective
search is robust to difference in object properties, and also to im-
age condition often related with specific objects (one example is
indoor/outdoor lighting).
If we compare with other algorithms, the second highest recall is
at 0.940 and is achieved by the jumping windows [34] using 10,000
boxes per class. As we do not have the exact boxes, we were unable
to obtain the MABO score. This is followed by the exhaustive
search of [12] which achieves a recall of 0.933 and a MABO of
0.829 at 100,352 boxes per class (this number is the average over
all classes). This is significantly lower then our method while using
at least a factor of 10 more object locations.
Note furthermore that the segmentation methods of [4, 9] have
a relatively high standard deviation. This illustrates that a single
strategy can not work equally well for all classes. Instead, using
multiple complementary strategies leads to more stable and reliable
If we compare the segmentation of Arbelaez [3] with a the sin-
gle best strategy of our method, they achieve a recall of 0.752 and
a MABO of 0.649 at 418 boxes, while we achieve 0.875 recall
and 0.698 MABO using 286 boxes. This suggests that a good seg-
mentation algorithm does not automatically result in good object
locations in terms of bounding boxes.
Figure 4 explores the trade-off between the quality and quantity
of the object hypotheses. In terms of recall, our “Fast” method out-
performs all other methods. The method of [16] seems competitive
for the 200 locations they use, but in their method the number of
boxes is per class while for our method the same boxes are used for
all classes. In terms of MABO, both the object hypotheses genera-
tion method of [4] and [9] have a good quantity/quality trade-off for
the up to 790 object-box locations per image they generate. How-
ever, these algorithms are computationally 114 and 59 times more
expensive than our “Fast” method.
Interestingly, the “objectness” method of [2] performs quite well
in terms of recall, but much worse in terms of MABO. This is
most likely caused by their non-maximum suppression, which sup-
presses windows which have more than an 0.5 overlap score with
an existing, higher ranked window. And while this significantly
improved results when a 0.5 overlap score is the definition of find-
ing an object, for the general problem of finding the highest quality
locations this strategy is less effective and can even be harmful by
eliminating better locations.
Figure 6 shows for several methods the Average Best Overlap
per class. It is derived that the exhaustive search of [12] which
uses 10 times more locations which are class specific, performs
similar to our method for the classes bike, table, chair, and sofa,
for the other classes our method yields the best score. In general,
the classes with the highest scores are cat, dog, horse, and sofa,
which are easy largely because the instances in the dataset tend
to be big. The classes with the lowest scores are bottle, person,
and plant, which are difficult because instances tend to be small.
0 500 1000 1500 2000 2500 3000
Number of Object Boxes
Harzallah et al.
Vedaldi et al.
Alexe et al.
Carreira and Sminchisescu
Endres and Hoiem
Selective search Fast
Selective search Quality
(a) Trade-off between number of object locations and the Pascal Recall criterion.
0 500 1000 1500 2000 2500 3000
Number of Object Boxes
Mean Average Best Overlap
Alexe et al.
Carreira and Sminchisescu
Endres and Hoiem
Selective search Fast
Selective search Quality
(b) Trade-off between number of object locations and the MABO score.
Figure 4: Trade-off between quality and quantity of the object hypotheses in terms of bounding boxes on the Pascal 2007 TE ST set. The
dashed lines are for those methods whose quantity is expressed is the number of boxes per class. In terms of recall “Fast” selective
search has the best trade-off. In terms of Mean Average Best Overlap the “Quality” selective search is comparable with [4, 9] yet is
much faster to compute and goes on longer resulting in a higher final MABO of 0.879.
(a) Bike: 0.863 (b) Cow: 0.874 (c) Chair: 0.884 (d) Person: 0.882 (e) Plant: 0.873
Figure 5: Examples of locations for objects whose Best Overlap score is around our Mean Average Best Overlap of 0.879. The green
boxes are the ground truth. The red boxes are created using the “Quality” selective search.
Average Best Overlap
Alexe et al.
Endres and Hoiem
Carreira and Sminchisescu
Felzenszwalb et al.
Selective search Fast
Selective search Quality
Figure 6: The Average Best Overlap scores per class for several method for generating box-based object locations on Pascal VOC 2007
TE ST. For all classes but table our “Quality” selective search yields the best locations. For 12 out of 20 classes our “Fast” selective
search outperforms the expensive [4, 9]. We always outperform [2].
Nevertheless, cow, sheep, and tv are not bigger than person and yet
can be found quite well by our algorithm.
To summarize, selective search is very effective in finding a high
quality set of object hypotheses using a limited number of boxes,
where the quality is reasonable consistent over the object classes.
The methods of [4] and [9] have a similar quality/quantity trade-off
for up to 790 object locations. However, they have more variation
over the object classes. Furthermore, they are at least 59 and 13
times more expensive to compute for our “Fast” and “Quality” se-
lective search methods respectively, which is a problem for current
dataset sizes for object recognition. In general, we conclude that
selective search yields the best quality locations at 0.879 MABO
while using a reasonable number of 10,097 class-independent ob-
ject locations.
5.2.2 Region-based Locations
In this section we examine how well the regions that our selective
search generates captures object locations. We do this on the seg-
mentation part of the Pascal VOC 2007 TES T set. We compare with
the segmentation of [3] and with the object hypothesis regions of
both [4, 9]. Table 6 shows the results. Note that the number of
regions is larger than the number of boxes as there are almost no
exact duplicates.
The object regions of both [4, 9] are of similar quality as our
“Fast” selective search, 0.665 MABO and 0.679 MABO respec-
tively where our “Fast” search yields 0.666 MABO. While [4, 9]
use fewer regions these algorithms are respectively 114 and 59
times computationally more expensive. Our “Quality” selective
search generates 22,491 regions and is respectively 25 and 13 times
faster than [4, 9], and has by far the highest score of 0.730 MABO.
method recall MABO # regions time(s)
[3] 0.539 0.540 ±0.117 1122 64
[9] 0.813 0.679 ±0.108 2167 226
[4] 0.782 0.665 ±0.118 697 432
Single Strategy 0.576 0.548 ±0.078 678 0.7
“Fast” 0.829 0.666 ±0.089 3574 3.8
“Quality” 0.904 0.730 ±0.093 22,491 17
[4, 9] + “Fast” 0.896 0.737 ±0.098 6,438 662
[4, 9] + “Quality” 0.920 0.758 ±0.096 25,355 675
Table 6: Comparison of algorithms to find a good set of potential
object locations in terms of regions on the segmentation part of
Pascal 2007 TE ST.
Figure 7 shows the Average Best Overlap of the regions per
class. For all classes except bike, our selective search consis-
tently has relatively high ABO scores. The performance for bike
is disproportionally lower for region-locations instead of object-
locations, because bike is a wire-frame object and hence very diffi-
cult to accurately delineate.
If we compare our method to others, the method of [9] is better
for train, for the other classes our “Quality” method yields similar
or better scores. For bird, boat, bus, chair, person, plant, and tv
scores are 0.05 ABO better. For car we obtain 0.12 higher ABO
and for bottle even 0.17 higher ABO. Looking at the variation in
ABO scores in table 6, we see that selective search has a slightly
lower variation than the other methods: 0.093 MABO for “quality”
and 0.108 for [9]. However, this score is biased because of the
wire-framed bicycle: without bicycle the difference becomes more
apparent. The standard deviation for the “quality” selective search
Average Best Overlap
Carreira and Sminchisescu
Endres and Hoiem
Selective search Fast
Selective search Quality
Figure 7: Comparison of the Average Best Overlap Scores per class
between our method and others on the Pascal 2007 TE ST set. Ex-
cept for train, our “Quality” method consistently yields better Av-
erage Best Overlap scores.
becomes 0.058, and 0.100 for [9]. Again, this shows that by relying
on multiple complementary strategies instead of a single strategy
yields more stable results.
Figure 8 shows several example segmentations from our method
and [4, 9]. In the first image, the other methods have problems
keeping the white label of the bottle and the book apart. In our
case, one of our strategies ignores colour while the “fill” similarity
(Eq. 5) helps grouping the bottle and label together. The missing
bottle part, which is dusty, is already merged with the table before
this bottle segment is formed, hence “fill” will not help here. The
second image is an example of a dark image on which our algo-
rithm has generally strong results due to using a variety of colour
spaces. In this particular image, the partially intensity invariant
Lab colour space helps to isolate the car. As we do not use the
contour detection method of [3], our method sometimes generates
segments with an irregular border, which is illustrated by the third
image of a cat. The final image shows a very difficult example, for
which only [4] provides an accurate segment.
Now because of the nature of selective search, rather than pitting
methods against each other, it is more interesting to see how they
can complement each other. As both [4, 9] have a very different
algorithm, the combination should prove effective according to our
diversification hypothesis. Indeed, as can be seen in the lower part
of Table 6, combination with our “Fast” selective search leads to
0.737 MABO at 6,438 locations. This is a higher MABO using less
locations than our “quality” selective search. A combination of [4,
9] with our “quality” sampling leads to 0.758 MABO at 25,355
locations. This is a good increase at only a modest extra number of
To conclude, selective search is highly effective for generating
object locations in terms of regions. The use of a variety of strate-
gies makes it robust against various image conditions as well as
the object class. The combination of [4], [9] and our grouping al-
gorithms into a single selective search showed promising improve-
ments. Given these improvements, and given that there are many
more different partitioning algorithms out there to use in a selec-
tive search, it will be interesting to see how far our selective search
paradigm can still go in terms of computational efficiency, number
of object locations, and the quality of object locations.
0.917 0.773 0.741
0.910 0.430 0.901
0.798 0.960 0.908
0.633 0.701 0.891
Selective Search [4] [9]
Figure 8: A qualitative comparison of selective search, [4], and [9].
For our method we observe: ignoring colour allows finding the
bottle, multiple colour spaces help in dark images (car), and not
using [3] sometimes result in irregular borders such as the cat.
5.3 Object Recognition
In this section we will evaluate our selective search strategy for
object recognition using the Pascal VOC 2010 detection task.
Our selective search strategy enables the use of expensive and
powerful image representations and machine learning techniques.
In this section we use selective search inside the Bag-of-Words
based object recognition framework described in Section 4. The re-
duced number of object locations compared to an exhaustive search
make it feasible to use such a strong Bag-of-Words implementa-
To give an indication of computational requirements: The pixel-
wise extraction of three SIFT variants plus visual word assignment
takes around 10 seconds and is done once per image. The final
round of SVM learning takes around 8 hours per class on a GPU for
approximately 30,000 training examples [33] resulting from two
rounds of mining negatives on Pascal VOC 2010. Mining hard
negatives is done in parallel and takes around 11 hours on 10 ma-
chines for a single round, which is around 40 seconds per image.
This is divided into 30 seconds for counting visual word frequen-
cies and 0.5 seconds per class for classification. Testing takes 40
seconds for extracting features, visual word assignment, and count-
ing visual word frequencies, after which 0.5 seconds is needed per
class for classification. For comparison, the code of [12] (without
cascade, just like our version) needs for testing slightly less than 4
seconds per image per class. For the 20 Pascal classes this makes
our framework faster during testing.
We evaluate results using the official evaluation server. This
evaluation is independent as the test data has not been released.
We compare with the top-4 of the competition. Note that while all
Participant Flat error Hierarchical error
University of Amsterdam (ours) 0.425 0.285
ISI lab., University of Tokyo 0.565 0.410
Table 8: Results for ImageNet Large Scale Visual Recognition
Challenge 2011 (ILSVRC2011). Hierarchical error penalises mis-
takes less if the predicted class is semantically similar to the real
class according to the WordNet hierarchy.
methods in the top-4 are based on an exhaustive search using vari-
ations on part-based model of [12] with HOG-features, our method
differs substantially by using selective search and Bag-of-Words
features. Results are shown in Table 7.
It is shown that our method yields the best results for the classes
plane, cat, cow, table, dog, plant, sheep, sofa, and tv. Except ta-
ble, sofa, and tv, these classes are all non-rigid. This is expected,
as Bag-of-Words is theoretically better suited for these classes than
the HOG-features. Indeed, for the rigid classes bike, bottle, bus,
car, person, and train the HOG-based methods perform better. The
exception is the rigid class tv. This is presumably because our se-
lective search performs well in locating tv’s, see Figure 6.
In the Pascal 2011 challenge there are several entries wich
achieve significantly higher scores than our entry. These methods
use Bag-of-Words as additional information on the locations found
by their part-based model, yielding better detection accuracy. In-
terestingly, however, by using Bag-of-Words to detect locations our
method achieves a higher total recall for many classes [10].
Finally, our selective search enabled participation to the detec-
tion task of the ImageNet Large Scale Visual Recognition Chal-
lenge 2011 (ILSVRC2011) as shown in Table 8. This dataset con-
tains 1,229,413 training images and 100,000 test images with 1,000
different object categories. Testing can be accelerated as features
extracted from the locations of selective search can be reused for
all classes. For example, using the fast Bag-of-Words framework
of [30], the time to extract SIFT-descriptors plus two colour vari-
ants takes 6.7 seconds and assignment to visual words takes 1.7
seconds 2. Using a 1x1, 2x2, and 3x3 spatial pyramid division it
takes 14 seconds to get all 172,032 dimensional features. Classifi-
cation in a cascade on the pyramid levels then takes 0.3 seconds per
class. For 1,000 classes, the total process then takes 323 seconds
per image for testing. In contrast, using the part-based framework
of [12] it takes 3.9 seconds per class per image, resulting in 3900
seconds per image for testing. This clearly shows that the reduced
number of locations helps scaling towards more classes.
We conclude that compared to an exhaustive search, selective
search enables the use of more expensive features and classifiers
and scales better as the number of classes increase.
5.4 Pascal VOC 2012
Because the Pacal VOC 2012 is the latest and perhaps final VOC
dataset, we briefly present results on this dataset to facilitate com-
parison with our work in the future. We present quality of boxes
using the TRAI N+VAL set, the quality of segments on the segmen-
tation part of TRAI N+VAL, and our localisation framework using a
Spatial Pyramid of 1x1, 2x2, 3x3, and 4x4 on the TEST set using
2We found no difference in recognition accuracy when using the Random Forest
assignment of [30] or kmeans nearest neighbour assignment in [32] on the Pascal
Boxes TRAIN +VAL 2012 MABO # locations
“Fast” 0.814 2006
“Quality” 0.886 10681
Segments TRAIN +VAL 2012 MABO # locations
“Fast” 0.512 3482
“Quality” 0.559 22073
Table 9: Quality of locations on Pascal VOC 2012 TRAI N+VAL.
the official evaluation server.
Results for the location quality are presented in table 9. We see
that for the box-locations the results are slightly higher than in Pas-
cal VOC 2007. For the segments, however, results are worse. This
is mainly because the 2012 segmentation set is considerably more
For the 2012 detection challenge, the Mean Average Precision is
0.350. This is similar to the 0.351 MAP obtained on Pascal VOC
5.5 An upper bound of location quality
In this experiment we investigate how close our selective search
locations are to the optimal locations in terms of recognition ac-
curacy for Bag-of-Words features. We do this on the Pascal VOC
2007 TE ST set.
0 2000 4000 6000 8000 10000
Number of Object Hypotheses
Mean Average Precision
SS "Quality"
SS "Quality" + Ground Truth
Figure 9: Theoretical upper limit for the box selection within our
object recognition framework. The red curve denotes the perfor-
mance using the top nlocations of our “quality” selective search
method, which has a MABO of 0.758 at 500 locations, 0.855 at
3000 locations, and 0.883 at 10,000 locations. The magenta curve
denotes the performance using the same top nlocations but now
combined with the ground truth, which is the upper limit of loca-
tion quality (MABO = 1). At 10,000 locations, our object hypoth-
esis set is close to optimal in terms of object recognition accuracy.
The red line in Figure 9 shows the MAP score of our object
recognition system when the top nboxes of our “quality” selective
search method are used. The performance starts at 0.283 MAP us-
ing the first 500 object locations with a MABO of 0.758. It rapidly
increases to 0.356 MAP using the first 3000 object locations with
a MABO of 0.855, and then ends at 0.360 MAP using all 10,097
object locations with a MABO of 0.883.
The magenta line shows the performance of our object recogni-
tion system if we include the ground truth object locations to our
System plane bike bird boat bottle bus car cat chair cow table dog horse motor person plant sheep sofa train tv
NLPR .533 .553 .192 .210 .300 .544 .467 .412 .200 .315 .207 .303 .486 .553 .465 .102 .344 .265 .503 .403
MIT UCLA [38] .542 .485 .157 .192 .292 .555 .435 .417 .169 .285 .267 .309 .483 .550 .417 .097 .358 .308 .472 .408
NUS .491 .524 .178 .120 .306 .535 .328 .373 .177 .306 .277 .295 .519 .563 .442 .096 .148 .279 .495 .384
UoCTTI [12] .524 .543 .130 .156 .351 .542 .491 .318 .155 .262 .135 .215 .454 .516 .475 .091 .351 .194 .466 .380
This paper .562 .424 .153 .126 .218 .493 .368 .461 .129 .321 .300 .365 .435 .529 .329 .153 .411 .318 .470 .448
Table 7: Results from the Pascal VOC 2010 detection task test set. Our method is the only object recognition system based on Bag-of-
Words. It has the best scores for 9, mostly non-rigid object categories, where the difference is up to 0.056 AP. The other methods are
based on part-based HOG features, and perform better on most rigid object classes.
hypotheses set, representing an object hypothesis set of “perfect”
quality with a MABO score of 1. When only the ground truth boxes
are used a MAP of 0.592 is achieved, which is an upper bound
of our object recognition system. However, this score rapidly de-
clines to 0.437 MAP using as few as 500 locations per image. Re-
markably, when all 10,079 boxes are used the performance drops
to 0.377 MAP, only 0.017 MAP more than when not including the
ground truth. This shows that at 10,000 object locations our hy-
potheses set is close to what can be optimally achieved for our
recognition framework. The most likely explanation is our use of
SIFT, which is designed to be shift invariant [21]. This causes ap-
proximate boxes, of a quality visualised in Figure 5, to be still good
enough. However, the small gap between the “perfect” object hy-
potheses set of 10,000 boxes and ours suggests that we arrived at
the point where the degree of invariance for Bag-of-Words may
have an adverse effect rather than an advantageous one.
The decrease of the “perfect” hypothesis set as the number of
boxes becomes larger is due to the increased difficulty of the prob-
lem: more boxes means a higher variability, which makes the ob-
ject recognition problem harder. Earlier we hypothesized that an
exhaustive search examines all possible locations in the image,
which makes the object recognition problem hard. To test if se-
lective search alleviates the problem, we also applied our Bag-of-
Words object recognition system on an exhaustive search, using
the locations of [12]. This results in a MAP of 0.336, while the
MABO was 0.829 and the number of object locations 100,000 per
class. The same MABO is obtained using 2,000 locations with se-
lective search. At 2,000 locations, the object recognition accuracy
is 0.347. This shows that selective search indeed makes the prob-
lem easier compared to exhaustive search by reducing the possible
variation in locations.
To conclude, there is a trade-off between quality and quantity of
object hypothesis and the object recognition accuracy. High qual-
ity object locations are necessary to recognise an object in the first
place. Being able to sample fewer object hypotheses without sac-
rificing quality makes the classification problem easier and helps
to improves results. Remarkably, at a reasonable 10,000 locations,
our object hypothesis set is close to optimal for our Bag-of-Words
recognition system. This suggests that our locations are of such
quality that features with higher discriminative power than is nor-
mally found in Bag-of-Words are now required.
6 Conclusions
This paper proposed to adapt segmentation for selective search. We
observed that an image is inherently hierarchical and that there are
a large variety of reasons for a region to form an object. Therefore
a single bottom-up grouping algorithm can never capture all possi-
ble object locations. To solve this we introduced selective search,
where the main insight is to use a diverse set of complementary and
hierarchical grouping strategies. This makes selective search sta-
ble, robust, and independent of the object-class, where object types
range from rigid ( to non-rigid (, and theoretically
also to amorphous (e.g.water).
In terms of object windows, results show that our algorithm is
superior to the “objectness” of [2] where our fast selective search
reaches a quality of 0.804 Mean Average Best Overlap at 2,134 lo-
cations. Compared to [4, 9], our algorithm has a similar trade-off
between quality and quantity of generated windows with around
0.790 MABO for up to 790 locations, the maximum that they gen-
erate. Yet our algorithm is 13-59 times faster. Additionally, it cre-
ates up to 10,097 locations per image yielding a MABO as high as
In terms of object regions, a combination of our algorithm with
[4, 9] yields a considerable jump in quality (MABO increases from
0.730 to 0.758), which shows that by following our diversification
paradigm there is still room for improvement.
Finally, we showed that selective search can be successfully used
to create a good Bag-of-Words based localisation and recognition
system. In fact, we showed that quality of our selective search lo-
cations are close to optimal for our version of Bag-of-Words based
object recognition.
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... Region proposal networks are mainly used in the first stage of the region-based object detector by distinguishing objects from the background. In previous works, there are Selective Search method [1] which adapts segmentation with exhaustive search and Edge Boxes method [2] which generates bounding boxes from the edges. In Ren et al. [3], the Region Proposal Network(RPN) which predicts objectness score and coordinates of proposals with multiple scales and ratios of the anchors. ...
... We plot recall rates of RPN trained with nRPN model and other region proposal models according to number of proposals and IoU threshold in Fig. 3. For comparing models, we use Bing [13], CPMC [14], EdgeBoxes [2], Endres [15], Geodesic [16], MCG [17], Objectness [18], Rahtu [19], RandomizedPrims [5], Rantalankila [20], Rigor [21], Se-lectiveSearch [1], Gaussian, SlidingWindow, Superpixels, Uiform [5], RPN [3]. ...
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... The approach is based on transfer learning and fine-tuning of a pre-trained CNN. It also uses the selective search (SS) algorithm proposed by Uijlings et al. [12], which combines segmentation and exhaustive search methods. SS was used in the pre-processing step to generate candidate regions within the image dataset. ...
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... UP-DETR [20] proposes a pretraining task where random patches are extracted from an image and a network is trained to localize them in the same image. DETReg [5] proposes to train an object detector to predict bounding boxes generated from the selective search algorithm [62] and to mimic the output of a network trained with contrastive learning. ...
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The behavior of animals reflects their internal state. Changes in behavior, such as a lack of sleep, can be detected as early warning signs of health issues. Zoologists are often required to use video recordings to study animal activity. These videos are generally not sufficiently indexed, so the process is long and laborious, and the observation results may vary between the observers. This study looks at the difficulty of measuring elephant sleep stages from surveillance videos of the elephant bran at night. To assist zoologists, we propose using deep learning techniques to automatically locate elephants in each camera surveillance, then mapping the elephants detected onto the barn plan. Instead of watching all of the videos, zoologists will examine the mapping history, allowing them to measure elephant sleeping stages faster. Overall, our approach monitors elephants in their barn with a high degree of accuracy.
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We study the question of feature sets for robust visual object recognition, adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally that grids of Histograms of Oriented Gradient (HOG) descriptors significantly outperform existing feature sets for human detection. We study the influence of each stage of the computation on performance, concluding that fine-scale gradients, fine orientation binning, relatively coarse spatial binning, and high-quality local contrast normalization in overlapping descriptor blocks are all important for good results. The new approach gives near-perfect separation on the original MIT pedestrian database, so we introduce a more challenging dataset containing over 1800 annotated human images with a large range of pose variations and backgrounds.
Image category recognition is important to access visual information on the level of objects and scene types. So far, intensity-based descriptors have been widely used for feature extraction at salient points. To increase illumination invariance and discriminative power, color descriptors have been proposed. Because many different descriptors exist, a structured overview is required of color invariant descriptors in the context of image category recognition. Therefore, this paper studies the invariance properties and the distinctiveness of color descriptors (software to compute the color descriptors from this paper is available from in a structured way. The analytical invariance properties of color descriptors are explored, using a taxonomy based on invariance properties with respect to photometric transformations, and tested experimentally using a data set with known illumination conditions. In addition, the distinctiveness of color descriptors is assessed experimentally using two benchmarks, one from the image domain and one from the video domain. From the theoretical and experimental results, it can be derived that invariance to light intensity changes and light color changes affects category recognition. The results further reveal that, for light intensity shifts, the usefulness of invariance is category-specific. Overall, when choosing a single descriptor and no prior knowledge about the data set and object and scene categories is available, the OpponentSIFT is recommended. Furthermore, a combined set of color descriptors outperforms intensity-based SIFT and improves category recognition by 8 percent on the PASCAL VOC 2007 and by 7 percent on the Mediamill Challenge.
This paper presents a unified framework for object detection, segmentation, and classification using regions. Region features are appealing in this context because: (1) they encode shape and scale information of objects naturally; (2) they are only mildly affected by background clutter. Regions have not been popular as features due to their sensitivity to segmentation errors. In this paper, we start by producing a robust bag of overlaid regions for each image using Arbeldez et al., CVPR 2009. Each region is represented by a rich set of image cues (shape, color and texture). We then learn region weights using a max-margin framework. In detection and segmentation, we apply a generalized Hough voting scheme to generate hypotheses of object locations, scales and support, followed by a verification classifier and a constrained segmenter on each hypothesis. The proposed approach significantly outperforms the state of the art on the ETHZ shape database(87.1% average detection rate compared to Ferrari et al. 's 67.2%), and achieves competitive performance on the Caltech 101 database.
We present a discriminative Hough transform based object detector where each local part casts a weighted vote for the possible locations of the object center. We show that the weights can be learned in a max-margin framework which directly optimizes the classification performance. The discriminative training takes into account both the codebook appearance and the spatial distribution of its position with respect to the object center to derive its importance. On various datasets we show that the discriminative training improves the Hough detector. Combined with a verification step using a SVM based classifier, our approach achieves a detection rate of 91.9% at 0.3 false positives per image on the ETHZ shape dataset, a significant improvement over the state of the art, while running the verification step on at least an order of magnitude fewer windows than in a sliding window approach.