Access to this full-text is provided by Springer Nature.
Content available from EURASIP Journal on Image and Video Processing
This content is subject to copyright. Terms and conditions apply.
R E S E A R C H Open Access
Scene search based on the adapted
triangular regions and soft clustering to
improve the effectiveness of the visual-
bag-of-words model
Zahid Mehmood
1
, Naila Gul
2
, Muhammad Altaf
2
, Toqeer Mahmood
3
, Tanzila Saba
4
, Amjad Rehman
5
and Muhammad Tariq Mahmood
6*
Abstract
The storage size of the image and video repositories are growing day by day due to the extensive use of digital
image acquisition devices. The position of an object within an image is obtained by analyzing the content-based
properties like shape, texture, and color, while compositional properties present the image layout and include the
photographic rule of composition. The high-quality images are captured on the basis of the rule of thirds that
divide each image into nine square areas. According to this rule, salient objects of an image are placed on the
intersection points or along the imagery lines of the grid to capture the position of the salient objects. To improve
image retrieval performance, visual-bag-of-words (VBoW) framework-based image representation is widely used
nowadays. According to this framework, the spatial relationship between salient objects of an image is lost due to
the formation of a global histogram of the image. This article presents a novel adapted triangular area-based technique,
which computes local intensity order pattern (LIOP) features, weighted soft codebooks, and triangular histograms from
the four triangular areas of each image. The proposed technique adds the spatial contents from four adapted triangular
areas of each image to the inverted index of the VBoW framework, solve overfitting problem of the larger sizes of the
codebook, and overwhelmed the problem of the semantic gap. The experimental results and statistical analysis
performed on five image collections show an encouraging robustness of the proposed technique that is compared
with the recent CBIR techniques.
Keywords: Content-based image retrieval, Support vector machine, Deep learning, Adapted triangular features
1 Introduction
The extensive use of the social networking sites and
technological improvements of the image acquisition de-
vices, as well as the size of image repositories, are in-
creased exponentially. It has gained the researchers’
interest to find a better approach to search images from
huge image collections using an effective and efficient
mechanism [1–3]. The commonly used image annota-
tion methods are based on the mapping of image de-
scriptors with few keywords. These methods cannot
describe the diversity of contents within images due to
the lack of discriminative capability. Content-based
image retrieval (CBIR) gained significance in research
over the years [4,5]. The focus of any CBIR technique is
to compute low-level visual features from the images
and extend the association between them in terms of
co-occurrence of similar visual contents. The visual con-
tents of the image are represented in terms of the
low-level visual features such as texture and shape [6].
Texture-based features are capable to find the spatial
variations between intensity values and surface attributes
of an object within an image. However, segmentation of
texture is a challenging task in order to fulfill human
perception [7]. Color-based features are invariant to
scale and rotation with high computational cost.
Shape-based features are not able to provide a
* Correspondence: tariq@koreatech.ac.kr
6
School of Computer Science and Engineering, Korea University of Technology
and Education, Cheonan 330-708, Republic of Korea
Full list of author information is available at the end of the article
EURASIP Journal on Image
and Video Processing
© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made.
Mehmood et al. EURASIP Journal on Image and Video Processing (2018) 2018:48
https://doi.org/10.1186/s13640-018-0285-7
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
mathematical foundation for image deformation and
generally consistent with the intuitive feeling [4]. There-
fore, image representation by using only low-level fea-
tures cannot describe the semantic relationship between
images efficiently. The resemblance in the pictorial look
of the images belonging to various categories conse-
quence in the closeness of low-level visual features and
it decreases the performance of CBIR [8,9].
In VBoW framework [10], features are computed from
each image, after that clustering is applied to formulate
codebook that consists of visual words. These visual
words are used to build a global histogram for each
image, which result in loss of the spatial information and
after that classification is performed, and the similarity is
calculated between inquiry image and archive images in
order to retrieve images. The robustness of the
VBoW-based image illustration suffers due to ignorance
of spatial context among local features [11–14]. Different
techniques such as geometric coding [15] and
co-occurrence of visual words [16] are introduced to
add the spatial context of visual words to the VBoW
framework. These techniques required high computa-
tional complexity on larger sizes of the codebook or dic-
tionary [11]. The spatial information is available in the
sub-areas of an image. The technique of Lazebnik et al.
[17] formulates spatial histogram from each square area
of the grid by splitting an image into different square
areas. Keeping in view the effective performance of [17]
to incorporate spatial information, the proposed tech-
nique of this article is to split an image into four adapted
triangular regions instead of square regions [17] and
extracting adapted local features, formulating weighted
soft codebooks, and computing histograms over the each
triangular area of an image.
The content-based properties of an image describe
the presence of salient objects in the image, while the
compositional properties describe the image layout
and it includes the photographic rule of composition
[18]. The principle of the rule of thirds is to divide
each image into nine square areas and place the salient
objects at the intersection points of the grid to acquire
content-based and compositional properties which im-
prove the CBIR performance [18]. Figure 1arepresents
an image that is captured according to the photo-
graphic rule of thirds, and salient objects are placed at
the intersection points of the square areas. Figure 1b
also represents an image that is separated into four tri-
angular areas to extract features over four dense scales
(from four distributed triangular areas) for the
addition of spatial information to the VBoW
Fig. 1 Image ahttp://mikemellizaphotography.blogspot.com/ is showing the photographic rule of thirds, while image bis showing its division
into four adapted triangular areas
Fig. 2 aImage with very close visual appearance but with a different semantic meaning. bThe image on the left is the inquiry image, while the
image of building and lady are the retrieved images in reply to the inquiry image due to close visual appearance
Mehmood et al. EURASIP Journal on Image and Video Processing (2018) 2018:48 Page 2 of 16
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
framework and to reduce the semantic gap. In an
image of a scene, there are different regions or objects
that are located in different sub-regions. The water or
grass is likely to be located at the bottom and clouds
or sky is positioned at the top, while salient objects are
positioned at left or right side. The detachment of an
image into four adapted triangular areas represents
this triangular association.
Figure 2a presents two images with a very close visual
similarity but different semantic meanings. The feature
map of the dog and the lady are visually analogous, but
their semantic implications are entirely diverse. In
Fig. 2b, the image on the left is the inquiry image, the
output of image retrieval can give emphasis on the con-
temporary building so the preeminent contest could be a
modern building like the one in the middle image, while
in some cases the emphasis may be on the prettiness, so
the rightmost image is the preeminent contest. The best
CBIR system is the one which retrieves accurate images
according to the user requirements. Different regions
like the ground, water, sky, clouds, and grass are located
within these different triangular areas, while the salient
objects like peoples and horses are positioned at left or
right side. Keeping these facts into view, we computed
dense LIOP features from four adapted triangular areas;
feature space is quantized to formulate weighted soft
Fig. 3 Framework of the proposed technique based on the adapted triangular areas and weighted soft codebooks
Fig. 4 Image ais chosen from the image benchmark of the Corel-1K, which presents the division into four adapted triangular areas, and image b
is presenting the method for computation of spatial histograms over the four adapted triangular areas of the image [20,51]
Mehmood et al. EURASIP Journal on Image and Video Processing (2018) 2018:48 Page 3 of 16
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
codebook from each triangular area and computed
spatial histogram from each adapted triangular area of
the image. The proposed technique provides an option
to extract the spatial properties of an image from each
of the triangular areas and solve the problem of overfit-
ting on larger sizes of the codebook by formulating
weighted soft codebooks. Following are the key contri-
butions of this article:
1. The accumulation of the spatial information to the
VBoW framework.
2. An adapted triangular regions based technique
for the feature extraction over four adapted
triangular regions of an image, resolve the problem
of overfitting on larger sizes of the codebook by
formulating weighted soft codebook over each
triangular region.
3. Lessening of semantic gap between high-level
semantic perceptions and low-level features of
the image.
2 Related work
The query by image content (QBIC) system [19] is the
first CBIR system introduced by IBM. Many systems
after QBIC are developed by IBM. Common domain for
all of these was to enhance the image searching tech-
niques and similarity matching in order to increase the
performance of CBIR. In existing literature [20,21], sev-
eral methods have been implemented to overcome the
limitations in CBIR, i.e., to reduce the semantic gap be-
tween low-level features and high-level semantic con-
cepts. To address these issues, focus of research is local
features such as color, boundary contour, texture, and
spatial layout, and different discriminative feature extrac-
tion techniques were introduced to enhance the per-
formance of CBIR.
An optimized technique for image retrieval is intro-
duced by Zhong and Defée [22], which relies on the
quantized histograms of discrete cosine transform
(DCT) blocks and uses different global parameters such
as scalar quantization, histograms size, difference vec-
tors, and integrated AC-pattern and DC-DirecVec histo-
grams. Histograms are optimized through the factor of
quantization together with the count of DCT blocks that
are normalized under luminance in order to improve
CBIR performance. Yuan et al. [23] propose a local de-
scriptor that integrates SIFT and LBP features to obtain
a high-dimensional feature vector for each image. Two
fusion models, i.e., patch-level and image-level, are
employed for feature fusion. For compact representation
of high-dimensional feature vector, clustering technique
based on k-means is used to formulate a codebook. Ac-
cording to the semantic category of the query image, im-
ages are retrieved and ranked based on the similarity
measure. Yu et al. [24] present feature fusion technique
which uses a histogram of oriented gradients (HOG),
SIFT, and local binary pattern (LBP) features in order to
achieve effective results for CBIR. A high-dimensional
Fig. 5 Image ais chosen from the semantic class “Horses”of the Corel-1K image benchmark, and image bis presenting the division of the
image into four rectangular areas for computation of histogram from each rectangular area [40]
Fig. 6 Sample images associated with image benchmark of the Corel-1K [40]
Mehmood et al. EURASIP Journal on Image and Video Processing (2018) 2018:48 Page 4 of 16
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
feature descriptor is formed by the fusion of separately
computed visual features using SIFT, LBP, and HOG fea-
tures. After that, these fused features are encoded into vis-
ual words by applying a k-means clustering technique to
form a dictionary and each image is characterized as a dis-
tribution of these visual words. Raja and Bhanu [25]
propose an improve CBIR technique on the basis of image
local features like color, texture, shape, wavelet-based
histogram, and incorporate relevance feedback to achieve
better accuracy for CBIR. A visual similarity matching
technique known as adaptive region matching (ARM) is
proposed by Yang and Cai [26] which uses region-based
image retrieval (RBIR). A semantic meaningful region
(SMR) and region important index (RII) are built to re-
duce the adverse consequence of interference regions and
loss of spatial information. Images are compared conform-
ing to whether the given image has an SMR and it
performs SMR-to-image matching to improve the per-
formance of the CBIR.
Wang et al. [27] propose a spatial weighting bag-of-fea-
tures (SWBoF) model of visual words by applying texture
measure. The spatial information is extracted from diverse
areas of the image. The dissimilarity between groups of
pixels is selected to compute the useful information. The
spatial information is computed by applying local entropy,
adjacent blocks distance and local variance. According to
the experimental results of [27], SWBOF model performs
better than traditional BoF approach. According to Liu et al.
[11], the spatial information among local features carries sig-
nificant information for content verification. A rotation and
scale-invariant edge orientation difference histogram
(EODH) descriptor are proposed by Tian et al. [28]. The
steerable filter and vector sum are applied to obtain the
main orientation of pixels. The color-SIFT and EODH de-
scriptors are integrated to improve the effectiveness of the
feature space and to reduce the semantic gap. The diction-
ary is constructed by applying a weighted average of
Color-SIFT and EODH. According to the experimental re-
sults [28], weighted average distribution enhances the per-
formance of the image retrieval.
Rashno et al. [29] propose an effective technique for
CBIR which relies on the discrete wavelet transform
(DWT) and color features. In this technique, visual con-
tents of each image are represented through a feature
vector which comprises of texture feature by applying
wavelet transform and color feature obtained by convert-
ing each image from RGB and HSV space. In wavelet
transform, each image is decomposed into four
Table 1 MAP performance on the image benchmark of the Corel-1K with a step size = 10
Features %
per image
for
dictionary
formation
MAP performance analysis on different dictionary sizes
20 60 100 200 400
10% 80.98 83.42 85.14 87.03 86.81
25% 81.08 83.58 85.21 87.14 86.68
50% 81.41 83.34 85.33 87.19 86.54
75% 81.78 83.78 85.22 87.22 86.64
100% 81.92 83.86 85.46 87.21 86.51
MAP 81.43 83.59 85.27 87.15 86.63
Std. dev. 0.41 0.22 0.12 0.16 0.11
Conf. interval 80.91–81.94 83.31–83.87 85.11–85.42 87.01–87.42 86.48–86.78
Std. error 0.18 0.10 0.05 0.07 0.05
Fig. 7 Effect on the MAP performance by varying weighted soft
codebook sizes on the image benchmark of the Corel-1K
Mehmood et al. EURASIP Journal on Image and Video Processing (2018) 2018:48 Page 5 of 16
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
sub-bands and then low-frequency sub-band is used as a
texture-feature. For color-features, dominant color de-
scriptor (DCD) is used for quantization of the image to
achieve color statistics and histogram features. Ant col-
ony optimization technique is used for selecting relevant
and unique features from the entire feature set consist-
ing of both color and texture features. The images are
retrieved by applying Euclidean distance to find a resem-
blance between inquiry image and database images.
Rahimi and Moghaddam [30] introduce a CBIR tech-
nique which uses intraclass and interclass features to im-
prove the performance of the CBIR. The distribution of
the color tone is used as an intraclass feature, whereas
singular value decomposition (SVD) and complex wave-
let transform are used as inter-class features.
Self-organizing map (SOM) is produced using these fea-
tures by applying artificial neural network (ANN) to in-
crease the proficiency of the CBIR. Yan et al. [31]
propose a novel technique using the deep convolutional
neural network to analyze image contents to learn
high-quality binary codes, known as a one-stage super-
vised deep hashing framework (SDHP). The proposed
technique assign similar binary codes to the similar im-
ages and vice versa. The learned codes in this technique
are evenly distributed, and during the conversion
process of the Euclidean space to Hamming space,
quantization loss is reduced. The discriminative power
of the learned binary codes is further improved by ex-
tending SDHP to SDHP+, which significantly improve
the search accuracy as compared with state-of-the-art
hashing algorithms. Yan et al. [32] present another novel
framework for recognition of Uyghur language text in
case of intricate background images. For detecting text
regions, the maximally stable extremal regions (MSERs)
technique is introduced but one of its shortcomings is
that in case of blur and low contrast images, it does not
perform well. Due to this reason, another technique is
introduced, known as channel-enhanced MSERs. This
technique outperforms the traditional MESRs technique,
but one limitation occurs in this case that is noise and
overlapping regions. The HOG and SVM are employed
for extracting non-text overlapping regions and noise.
One of the most important outcomes of this technique
is the usefulness for detecting Uyghur language as well
as other languages can be identified by changing some
empirical rules and parameters. Different efficient tech-
niques are introduced to analyze the image and video
contents in a variety of applications [33,34].
3 Proposed methodology
The framework of the proposed technique is presented
in Fig. 3. We obtained the spatial information by separ-
ating an image into four adapted triangular areas. This
enables to extract the visual features based on LIOP
Table 2 MAP performance comparison of the proposed technique with recent CBIR techniques on the image benchmark of the
Corel-1 K
Class name Proposed technique with step size = 10 [43][42][41][28][44]
Africa 70.22 73 65 63.50 74.60 58.73
Beach 78.95 72 70 64.20 37.80 48.94
Buildings 80.89 79 75 69.80 53.90 53.74
Busses 97.87 100 95 91.50 96.70 95.81
Dinosaurs 99.29 97 100 99.20 99 98.36
Elephants 94.65 75 80 78.10 65.90 64.17
Flowers 89.11 86 95 94.80 91.20 85.64
Horses 97.04 82 90 95.20 86.90 80.31
Mountains 81.84 69 75 73.80 58.50 54.27
Food 82.25 90 75 80.60 62.20 63.14
MAP 87.22 82.30 82.00 81.07 72.67 70.31
Table 3 Average-recall performance comparison of the proposed
technique with recent CBIR techniques on the image benchmark
of the Corel-1K
Class name Proposed technique
with step size = 10
[43][42][41][28][44]
Africa 14.04 14.6 13 12.7 14.92 11.75
Beach 15.79 14.4 14 12.84 7.56 9.79
Buildings 16.18 15.8 15 13.96 10.78 10.75
Busses 19.57 20 19 18.3 19.34 19.16
Dinosaurs 19.86 19.4 20 19.84 19.8 19.67
Elephants 18.93 15 16 15.62 13.18 12.83
Flowers 17.82 17.2 19 18.96 18.24 17.13
Horses 19.41 16.4 18 19.04 17.38 16.06
Mountains 16.37 13.8 15 14.76 11.7 10.85
Food 16.45 18 15 16.12 12.44 12.63
Average 17.44 16.46 16.40 16.21 14.53 14.06
Mehmood et al. EURASIP Journal on Image and Video Processing (2018) 2018:48 Page 6 of 16
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
features, weighted soft codebooks, and spatial histo-
grams from the top, down, left, and right areas of the
image. Figure 3is presenting the procedure for compu-
tation of dense LIOP features, weighted soft codebooks,
and spatial histograms over the four triangular areas of
the image. The description of each step of the proposed
technique is as follows:
1. The approximation coefficient of each image
(represented by IMG) of the training and test sets
after applying level-2 decomposition using discrete
wavelet transform (DWT) is divided into the four
adapted triangular areas, which are extracted by ap-
plying following mathematical equations:
Rttp1¼IMG 1;1ðÞ;Rttp2¼IMG 1;wðÞ;Rttp3
¼IMG h
2;
w
2
ð1Þ
Rbtp1¼IMG h;1ðÞ;Rbtp2¼IMG h;wðÞ;Rbtp3
¼IMG h
2;
w
2
ð2Þ
Rltp1 ¼IMG 1;1ðÞ;Rltp2 ¼IMG h;1ðÞ;Rltp3
¼IMG h
2;
w
2
ð3Þ
Fig. 8 Top-20 retrieved images associated with “Mountains”class of the image benchmark of the Corel-1K
Mehmood et al. EURASIP Journal on Image and Video Processing (2018) 2018:48 Page 7 of 16
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Rrtp1¼IMG 1;wðÞ;Rrtp2¼IMG h;wðÞ;Rrtp3
¼IMG h
2;
w
2
ð4Þ
2. The LIOP features [35] are computed from each
adapted triangular area of the image by applying
following mathematical equations:
LIOP descriptor ¼des1;des2;…;desi
ðÞð5Þ
desl¼Xxεbinl
wxðÞLIOP xðÞ ð6Þ
where
LIOP xðÞ¼φðγPxðÞðÞ ð7Þ
where
PxðÞ¼ Ix
1
ðÞ;Ix
2
ðÞ;…;Ix
n
ðÞðÞϵPNð8Þ
and φis a feature mapping function that map the per-
mutation πto an N!−dimensional feature vector Vi
N!
whose all the elements are 0 except for the i
th
element
which is 1. The feature mapping function φis defined by
the following mathematically equation:
Fig. 9 Top-20 retrieved images associated with “Elephants”class of the image benchmark of the Corel-1K
Mehmood et al. EURASIP Journal on Image and Video Processing (2018) 2018:48 Page 8 of 16
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
ϕπðÞ¼VInd πðÞ
N!;πεYNð9Þ
where VIndðπÞ
N!¼ð0;…;0;1IndðπÞ;0;…;0Þand Ind(π)
represents the index of πin the index table.
LIOP xðÞ¼VIndðγPxðÞðÞ
N!
LIOP xðÞ¼ 0;…;0;1IndðγPxðÞðÞ
;0;…;0
ð10Þ
and wxðÞ¼
Xi;jsgn I xi
ðÞ−Ix
j
−Tlp
þ1ð11Þ
In the above equations, for a sample point x
n
,I(x
n
)
represents the intensity of the n
th
neighboring sample,
preset threshold is represented by T
lp
, sign function is
represented by sgn,w(x) represents the weighted
function of the LIOP descriptor, the feature mapping
function is represented by φ, and i,jrepresent the co-
ordinate position of the sample point x
n
.
3. The combination of visual words known as a
codebook. In order to formulate four weighted
soft codebooks, the clustering technique based on
the k-means++ [36] is applied on the extracted
features from each adapted triangular area, which
produces four soft codebooks. In order to resolve
the problem of overfitting on a codebook of larger
sizes, each soft codebook is multiplied by the
weight of 0.25 (as four weighted soft codebooks are
formulated, so 1/4 result in a weight (w) of 0.25),
which produces four weighted soft codebooks. The
clustering technique based on the k-means++ is
chosen for auto selection of initial seed for
clustering to improve the clustering results. The
four weighted soft codebooks are represented by
the following mathematical equations:
Cwst ¼0:25 vt1;vt2;vt3;vt4;vt5;;…;vtx
fgð12Þ
Cwsb ¼0:25 vb1;vb2;vb3;vb4;vb5;;…;vbx
fgð13Þ
Cwsl ¼0:25 vl1;vl2;vl3;vl4;vl5;;…;vlx
fgð14Þ
Cwsr ¼0:25 vr1;vr2;vr3;vr4;vr5;;…;vrx
fgð15Þ
where C
wst
,C
wsb
,C
wsl
,and C
wsr
represent the weighted soft
codebooks formulated from top, bottom, left, and right
adapted triangular areas of the image, respectively. The v
t1
Fig. 11 Effect on the MAP performance by varying weighted soft
codebook sizes on the image benchmark of the Corel-1.5K
Fig. 10 Sample images associated with image benchmark of the Corel-1.5K [40]
Mehmood et al. EURASIP Journal on Image and Video Processing (2018) 2018:48 Page 9 of 16
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
to v
tx
,v
b1
to v
bx
,v
l1
to v
lx
,andv
r1
to v
rx
represent the vis-
ual words of the top, bottom, left, and right adapted tri-
angular areas of the weighted soft codebooks, respectively.
4. The visual words of each adapted triangular area of
the image are mapped to the associated quantized
feature descriptor by applying following
mathematical equation:
va
n
ðÞ¼argminv∈catr Dist v;an
ðÞ ð16Þ
where v(a
n
) represents the associated visual word of the
n
th
feature descriptor a
n
. The distance between visual
word vand feature descriptor a
n
is represented by
Dist(v,a
n
). The c
atr
represents the weighted soft code-
book of the associated triangular area of the image.
5. The spatial histogram is formulated using an x
number of visual words of each adapted triangular
area of the weighted soft codebook as shown in
Fig. 4. The four adapted triangular areas of the
image produce four resultant spatial histograms.
6. The spatial histogram of each adapted triangular area
of the image is concatenated together, and spatial
information is added to the VBoW framework. Let
total number of visual words of each weighted soft
codebook (C
ws
) are represented by x.Ifvisualwordv
i
is mapped to the d
si
descriptor, then i
th
bin of each
spatial histogram h
i
is the cardinality of the set d
s
is
represented as follows:
hi¼Card dsi
ðÞ and dsi
¼vdsi;dsi∈1;…;xðÞjCws vdsi
ðÞ¼vi
fg
ð17Þ
7. The resultant spatial histogram of each image is
normalized by applying the Hellinger kernel
function [37] of the support vector machine (SVM).
The total images of each reported image benchmark
are categorized into training and test sets. The SVM
classifier is trained using normalized histograms of the
training images. The best values of the regularization
parameters (C and Gamma) are determined by
applying 10-fold cross validation using the images of
the training set for each reported image benchmark.
8. The Euclidean distance [38] is used as a similarity
measure technique. The images are retrieved by
measuring the similarity between a score of the
inquiry image and scores of the archive images for
each reported image benchmark.
Fig. 12 Sample images associated with image benchmark of the 15-Scene
Table 4 MAP performance and recall comparison of the proposed technique with recent CBIR techniques on the image benchmark
of the Corel-1.5K
Performance evaluation parameters Proposed technique with step size = 10 SQ + Spatiogram [45] GMM + mSpatiogram [45]
MAP 85.56 63.95 74.10
Recall 17.11 12.79 13.80
Mehmood et al. EURASIP Journal on Image and Video Processing (2018) 2018:48 Page 10 of 16
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
4 Experimental results and discussions
To evaluate the performance of proposed methodology,
we have selected five challenging image benchmarks and
results are compared with recent CBIR techniques. The
images are randomly divided into training and test sets.
Visual features are extracted by applying dense-LIOP
feature descriptor, and all the processing is performed
on the grayscale images.
Training images are used to formulate the weighted soft
codebooks, and test images are used to compute the re-
trieval precision. Due to the unsupervised nature of clus-
tering using k-means++, each experiment is repeated 10
times and the average values are reported and for every it-
eration, images are randomly divided into training and test
sets. Lazebnik et al. [17] proposed a CBIR technique based
on the spatial pyramid matching, which divides an image
into several rectangular grids and formulate histograms
from each region of the grid. The proposed technique of
image representation based on adapted triangular areas is
compared with 2 × 2 RSH technique of CBIR, which di-
vides an image into 2 × 2 rectangular grids. Figure 5is
presenting the division of an image into four rectangular
areas. The histograms are computed from each rectangular
region to perform a comparison between the spatial rect-
angular and adapted triangular histograms of visual words.
4.1 Experimental parameters and performance evaluation
metrics
The details about the parameters used for the experi-
mental research are given below:
a) Codebook size: The total number of images of each
reported image collection is divided into two sets
known as test and training sets. The images of the
training set are used to formulate the dictionary for
each image benchmark. The performance of the
CBIR techniques based on the VBoW model is
affected by varying sizes of the codebook.
b) Step size: Dense-LIOP is used for features extraction.
For a precise content-based image matching, we
extracted dense features from four divided triangular
regions of each image (at four different scales). Step
size is used to control the sampling density, which is
the vertical and horizontal displacement of each
feature center to the next. The proposed technique is
evaluated using pixel step sizes of 10, 15, and 25.
For a step size of 10, every 10th pixel is selected to
compute the LIOP descriptor.
c) Features % per image for dictionary learning:
According to [39], the number of features percentage
per image for codebook or dictionary learning from
the training set is an important parameter that affects
the performance of CBIR. We formulated dictionary
using different feature percentages [10, 25, 50, 75,
and 100%] per image of the training set.
Precision, recall, average precision (AP), and mean
average precision (MAP) are the standard performance
evaluation metrics to evaluate the performance of the
CBIR system. The performance of the proposed
technique is also evaluated using these metrics.
d) Precision: The specificity of the image retrieval
model is evaluated by the precision
‘
P
’
, which is
mathematically defined as follows:
P¼Cr
Rt
ð18Þ
Table 5 MAP performance and recall comparison of the proposed technique with recent CBIR techniques on the image benchmark
of the 15-Scene
Performance measures Proposed technique RSH technique [28] HOG 2 ×2 technique [52] Spatial level-2 technique (DBN) [51]
Precision 79.02 81.10 81.00 79.70
Recall 15.80 16.22 16.20 15.94
Fig. 13 Effect on the MAP performance by varying dictionary size
on the image benchmark of the 15-Scene
Mehmood et al. EURASIP Journal on Image and Video Processing (2018) 2018:48 Page 11 of 16
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
e) Recall: The recall ‘R’evaluate the sensitivity of the
image retrieval model, which is mathematically
defined as follows:
R¼Cr
Tc
ð19Þ
where R
t
,C
r
, and T
c
represent the total retrieved, cor-
rectly retrieved, and total per class images, respectively.
f) Average precision (AP): The AP for a set of image
queries is the average of the precision of particular
class of the image benchmark, which is
mathematically defined as follows:
AP ¼PM
j¼1PjðÞ
Mð20Þ
g) Mean average precision (MAP): For a set of image
queries, the MAP is the mean of the average
precision values for each image query, which is
mathematically defined as follows:
MAP ¼PM
j¼1AP jðÞ
Mð21Þ
where Mis the total number of the image queries.
4.2 Analysis of the evaluation metrics on the image
benchmark of the Corel-1K
The image benchmark of the Corel-1K is a subset of the
WANG image benchmark [40]. The proposed technique
is evaluated using image benchmark of the Corel-1K,
and recent CBIR techniques [28,41–44] are used for the
performance comparison of the proposed technique.
The image benchmark of the Corel-1K comprises of
1000 images, which are organized into 10 semantic clas-
ses. The sample image associated with each class of the
Corel-1K is presented in Fig. 6. The test set and a train-
ing set of the Corel-1K are split into 300 and 700 im-
ages, respectively. The MAP performance for top 20
image retrievals for a step size of 10 using different sizes
of the weighted soft codebooks and feature percentage for
weighted soft codebook learning are shown in Table 1.
The MAP is calculated by taking the mean of the
column-wise values of Table 1. The comparison of the
MAP performance using proposed technique and 2 × 2
RSH technique for the dense pixel strides of 10, 15, and
25 is presented in Fig. 7. According to the experimental
results, the MAP obtained by using the proposed re-
search with a pixel stride of 10 is 87.22%, while the
MAP obtained from the pixel strides of 15 and 25 is
84.27 and 78.13%, respectively (with a weighted soft
codebook size of 200 visual words). This shows that in-
creasing the pixel stride decreases the MAP performance
and vice versa. In order to present a sustainable per-
formance of the proposed research, the MAP for top-20
Fig. 15 Analysis of MAP performance comparison of the proposed technique with recent CBIR techniques [47–49] on the image benchmark of
the Ground-truth
Fig. 14 Sample images associated with image benchmark of the Ground-truth
Mehmood et al. EURASIP Journal on Image and Video Processing (2018) 2018:48 Page 12 of 16
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
retrievals is calculated and compared with recent CBIR
techniques [28,41–44]. Table 2and Table 3are present-
ing the class-wise comparisons of average precision and
recall on the image benchmark of the Corel-1K.
Experimental results and the comparisons are con-
ducted on the image benchmark of the Corel-1K prove
the robustness of the proposed technique. The mean
precision and recall values obtained using proposed
technique are higher than the recent CBIR techniques
[28,41–44]. The image retrieval results for the semantic
classes of “Mountains”and “Elephants”are presented in
Figs. 8and 9, respectively in response to the query im-
ages that show a reduction of semantic gap in terms of
classifier decision value (score). Top-20 retrieved images,
whose score is close to the score of the query image are
more similar to the query image and vice versa.
4.3 Analysis of the evaluation metrics on the image
benchmark of the Corel-1.5K
There are 15 semantic classes in image benchmark of
the Corel-1.5K, and each semantic class contains 100
images. The image benchmark of the Corel-1.5K is also
a subset of the WANG image benchmark [40] and used
for performance comparison of the proposed technique
with [45]. Figure 10 is presenting the sample of images
from each semantic class of the image benchmark of the
Corel-1.5K. The MAP performance as a function of the
weighted soft codebook size is graphically presented in
Fig. 11. The precision and recall values obtained using
the proposed technique are compared with recent CBIR
technique of [45] and are presented in Table 4.
According to the experimental results, the MAP per-
formance of 85.56% is obtained using the proposed tech-
nique on a weighted soft codebook size of 400 visual
words (with pixel step size of 10), while the MAP per-
formance obtained using 2 × 2 RSH technique is 84.97%.
The proposed image representation outperforms 2 × 2
RSH-based CBIR technique as well as recent CBIR tech-
nique of [45].
4.4 Analysis of the evaluation metrics on the image
benchmark of the 15-Scene
The 15-Scene image benchmark [46] comprises of 4485
images that are organized into 15 categories. Each cat-
egory comprises of 200 to 400 images containing out-
door and indoor scenes as shown in Fig. 12.The
resolution of each image in this image collection is
250 × 300 pixels. The MAP performance of the proposed
technique on different sizes of the dictionary is shown in
Fig. 13, which is compared with 2 × 2 RSH-based CBIR
technique.
Fig. 16 PR-curve of the proposed technique on the image benchmarks of the Corel-1K, Corel-1.5K, 15-Scene, Ground-truth, and Caltech-256
Table 6 MAP performance and recall comparison of the proposed
technique with recent CBIR techniques on the image benchmark of
the Caltech-256
Performance evaluation
parameters
Proposed
technique
MN-ARM
[53]
MN-MIN
[54]
DCT
[22]
MAP 31.19 28.21 26.89 23.91
Recall 06.24 05.64 5.37 04.78
Mehmood et al. EURASIP Journal on Image and Video Processing (2018) 2018:48 Page 13 of 16
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
The experimental details are shown in Fig. 13 and
Table 5that indicates the efficiency of the proposed
technique on the reported weighted soft codebook sizes
as compared with 2 × 2 RSH-based CBIR technique and
recent CBIR techniques [26,40,41]. The proposed tech-
nique and 2 × 2 RSH technique gives best MAP per-
formance of 79.02 and 77.99% by formulating the
weighted soft codebook size of 800 visual words using a
step size of 10, respectively.
4.5 Analysis of the evaluation metrics on the image
benchmark of the Ground-truth
In Ground-truth image benchmark, a total number of
images is 1109 which are organized into 22 semantic
classes. The Ground-truth image benchmark is com-
monly used for performance evaluation of the recent
CBIR techniques [47–49]. For a clear comparison, 5 se-
mantic classes which comprise a total number of 228
images are chosen as the performance of the recent
CBIR techniques [47–49] are also evaluated for the same
classes. The sample images of the chosen categories of
the Ground-truth image benchmark are shown in Fig. 14,
while MAP performance comparison of the proposed
technique with recent CBIR techniques [47–49] is pre-
sented in Fig. 15, which proves the robustness of the
proposed technique by formulating a weighted soft
codebook size of 60 words using a step size of 10.
4.6 Analysis of the evaluation metrics on the image
benchmark of the Caltech-256
The image benchmark of the Caltech-256 is deposited on
2007 and a successor of the image benchmark of the
Caltech-101. The total number of images in the
Caltech-256 are 30,607 which are categorized into 256 se-
mantic classes. Every semantic class contains a minimum
of 80 images with no artifacts, performance is halved, shows
diversity in image representation [50]. The best MAP per-
formance of the proposed technique is 31.19%, which is
achieved on a weighted soft codebook size of 1400 visual
words. The performance of the proposed technique is com-
pared with recent CBIR techniques [20,44,45], which also
proved its robustness as presented in Table 6.Theperform-
ance analysis in terms of the precision-recall (PR)-curve of
the proposed technique is presented in Fig. 16 for the
image benchmarks of the Corel-1K, Corel-1.5K, 15-Scene,
Ground-truth, and Caltech-256.
4.7 Requirement of the computational resources
The performance of the proposed technique of this art-
icle is measured on a computer, whose hardware specifi-
cations are as follows: RAM with 8 GB storage capacity,
GPU with 2 GB storage capacity, and Intel Pentium (R)
Core i7 microprocessor with 2.4 GHz clock frequency.
The required software resources for the implementation
of the proposed technique are Microsoft Windows 7
64-bit operating system and MATLAB 2015a. The com-
putational complexity required for feature extraction of
the proposed technique is presented in Table 7, while
computational complexity of the proposed image re-
trieval framework (i.e., complete framework) is presented
in Table 8. The computational complexity is reported by
selecting the image benchmark of the Corel-1 K, which
comprises of each image resolution of 384 × 256 or
256 × 384.
5 Conclusions
In this article, we have proposed a novel image represen-
tation based on the adapted triangular areas and
weighted soft codebooks. The dense LIOP features,
weighted soft codebooks, and spatial histograms are ex-
tracted over the four triangular areas of the image. The
proposed technique adds the spatial context of informa-
tion to the inverted index of VBoW model. The collec-
tion of dense LIOP features and spatial histograms over
the four adapted triangular areas of an image is a pos-
sible solution to add the spatial information to the
VBoW model and reduction of the semantic gap be-
tween low-level features of the image and high-level se-
mantic concepts. The problem of overfitting on the
codebook of larger sizes is reduced by the weighted soft
codebooks to further improve CBIR performance. The
Hellinger kernel of the SVM is selected for image classi-
fication. The proposed technique is evaluated on five
challenging image benchmarks and results are compared
with recent CBIR techniques and 2 × 2 RSH technique
of the CBIR. The proposed image representation outper-
forms in terms of the performance comparison with re-
cent CBIR techniques as well as RSH technique of the
Table 8 Computational complexity (time in seconds) of the
proposed image retrieval framework and its comparisons with
recent CBIR techniques
Total number of
retrieved images
Proposed
technique
Spatial level-2
technique [51]
Standard VBoW
technique
Top-05 0.3601 0.3726 0.3415
Top-10 0.4829 0.5178 0.4674
Top-15 0.6901 0.7050 0.6589
Top-20 0.8513 0.8882 0.8213
Top-25 1.0243 1.0599 0.9913
Table 7 Computational complexity (time in seconds) of the
proposed technique required for feature extraction only
Proposed
technique
RSHD technique [55] EODH
technique
[28]
Spatial level-
2 technique
[51]
Standard
VBoW
technique
RSHD CDH SHE
0.0788 0.3750 1.7090 0.1860 5.6 0.0821 0.0641
Mehmood et al. EURASIP Journal on Image and Video Processing (2018) 2018:48 Page 14 of 16
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
CBIR. In future, we plan to replace VBoW model with a
vector of locally aggregated descriptors (VLAD) or
Fisher kernel framework to evaluate the proposed tech-
nique for a large-scale image retrieval.
Abbreviations
ANN: Artificial neural network; ARM: Adaptive region matching;
CBIR: Content-based image retrieval; DCD: Dominant color descriptor;
DCT: Discrete cosine transform; DWT: Discrete wavelet transform;
EODH: Edge orientation difference histogram; HOG: Histogram of oriented
gradients; LBP: Local binary pattern; LIOP: Local intensity order pattern;
MAP: Mean average precision; MSERs: Maximally stable extremal regions;
QBIC: Query-by-image-content; RBIR: Region-based image retrieval;
RII: Region important index; RSH: Rectangular spatial histogram;
SDHP: Supervised deep hashing framework; SMR: Semantic meaningful
region; SOM: Self-organizing map; SVD: Singular value decomposition;
SVM: Support vector machine; SWBoF: Spatial weighting bag-of-features;
VBoW: Visual-bag-of-words; VLAD: Vector of locally aggregated descriptors
Funding
This work was supported by Basic Science Research Program through the
National Research Foundation of Korea (NRF) funded by the Ministry of
Education (2016R1D1A1B03933860).
Availability of data and materials
Data sharing is not applicable to this article as authors have used the
publically available datasets, whose details are included in Section 4of this
article. Please contact authors for further requests.
Authors’contributions
All the authors contributed equally. All authors read and approved the final
manuscript.
Ethics approval and consent to participate
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Publisher’sNote
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
Author details
1
Department of Software Engineering, University of Engineering and
Technology, Taxila 47050, Pakistan.
2
Department of Basic Sciences, University
of Engineering and Technology, Taxila 47050, Pakistan.
3
Department of
Computer Science, University of Engineering and Technology, Taxila 47050,
Pakistan.
4
College of Computer and Information Sciences, Prince Sultan
University, Riyadh 11586, Saudi Arabia.
5
College of Computer and
Information Systems, Al-Yamamah University, Riyadh 11512, Saudi Arabia.
6
School of Computer Science and Engineering, Korea University of Technology
and Education, Cheonan 330-708, Republic of Korea.
Received: 20 July 2017 Accepted: 25 May 2018
References
1. A Alzu’bi, A Amira, N Ramzan, Semantic content-based image retrieval: a
comprehensive study. J. Vis. Commun. Image Represent. 32,20–54 (2015).
2. A-M Tousch, S Herbin, J-Y Audibert, Semantic hierarchies for image
annotation: a survey. Pattern Recogn. 45(1), 333–345 (2012).
3. D Zhang, MM Islam, G Lu, A review on automatic image annotation
techniques. Pattern Recogn. 45(1), 346–362 (2012).
4. Hiremath, P. and J. Pujari. Content based image retrieval using color, texture
and shape features. in Advanced Computing and Communications, 2007.
ADCOM 2007. International Conference on. 2007. IEEE.
5. T Wan, Z Qin, An application of compressive sensing for image fusion. Int. J.
Comput. Math. 88(18), 3915–3930 (2011).
6. T Ojala, M Pietikainen, T Maenpaa, Multiresolution gray-scale and rotation
invariant texture classification with local binary patterns. IEEE Trans. Pattern
Anal. Mach. Intell. 24(7), 971–987 (2002).
7. Takala, V., T. Ahonen, and M. Pietikäinen, Block-based methods for image
retrieval using local binary patterns.Image Analysis, 2005: p. 13–181.
8. Z Mehmood, SM Anwar, N Ali, HA Habib, M Rashid, A novel image retrieval
based on a combination of local and global histograms of visual words.
Math. Probl. Eng. 2016(2016), 1–12 (2016).
9. Z Mehmood, S Anwar, M Altaf, A novel image retrieval based on rectangular
spatial histograms of visual words. Kuwait J. Sci. 45(1), 54–69 (2018).
10. Sivic, J. and A. Zisserman. Video Google: a text retrieval approach to object
matching in videos.innull. 2003. IEEE.
11. Z Liu, H Li, W Zhou, R Zhao, Q Tian, Contextual hashing for large-scale
image search. IEEE Trans. Image Process. 23(4), 1606–1614 (2014).
12. Philbin, J., O. Chum, M. Isard, J. Sivic, and A. Zisserman. Object retrieval with
large vocabularies and fast spatial matching. in Computer Vision and Pattern
Recognition, 2007. CVPR'07. IEEE Conference on. 2007. IEEE.
13. M Yousuf, Z Mehmood, HA Habib, T Mahmood, T Saba, A Rehman, M Rashid,
A novel technique based on visual words fusion analysis of sparse features for
effective content-based image retrieval. Math. Probl. Eng. 2018,1–13 (2018).
14. S Jabeen, Z Mehmood, T Mahmood, T Saba, A Rehman, MT Mahmood, An
effective content-based image retrieval technique for image visuals
representation based on the bag-of-visual-words model. PLoS One 13(4),
e0194526 (2018).
15. Zhou, W., H. Li, Y. Lu, and Q. Tian, SIFT match verification by geometric
coding for large-scale partial-duplicate web image search. ACM Transactions
on Multimedia Computing, Communications, and Applications (TOMM),
2013. 9(1): p. 4.
16. Khan, R., C. Barat, D. Muselet, and C. Ducottet. Spatial orientations of visual
word pairs to improve bag-of-visual-words model.inProceedings of the British
Machine Vision Conference. 2012. BMVA Press.
17. Lazebnik, S., C. Schmid, and J. Ponce. Beyond bags of features: spatial pyramid
matching for recognizing natural scene categories. in Computer vision and
pattern recognition, 2006 IEEE computer society conference on. 2006. IEEE.
18. H Zhang, M Gönen, Z Yang, E Oja, Understanding emotional impact of images
using Bayesian multiple kernel learning. Neurocomputing 165,3–13 (2015).
19. M Flickner, H Sawhney, W Niblack, J Ashley, Q Huang, B Dom, M Gorkani, J
Hafner, D Lee, D Petkovic, Query by image and video content: the QBIC
system. Computer 28(9), 23–32 (1995).
20. Z Mehmood, T Mahmood, MA Javid, Content-based image retrieval and semantic
automatic image annotation based on the weighted average of triangular
histograms using support vector machine. Appl. Intell. 48(1), 166–181 (2018).
21. Mehmood, Z., F. Abbas, T. Mahmood, M.A. Javid, A. Rehman, and T. Nawaz,
Content-based image retrieval based on visual words fusion versus features
fusion of local and global features.Arab. J. Sci. Eng., 2018: p. 1–20.
22. D Zhong, I Defée, DCT histogram optimization for image database retrieval.
Pattern Recogn. Lett. 26(14), 2272–2281 (2005).
23. X Yuan, J Yu, Z Qin, T Wan, in IEEE International Conference on Image Processing.
A SIFT-LBP image retrieval model based on bag of features (2011).
24. J Yu, Z Qin, T Wan, X Zhang, Feature integration analysis of bag-of-features
model for image retrieval. Neurocomputing 120, 355–364 (2013).
25. NMK Raja, KS Bhanu, Content bases image search and retrieval using
indexing by KMeans clustering technique. Int. J. Adv. Res. Comp. Comm.
Eng. 2(5), 2181–2189 (2013).
26. X Yang, L Cai, Adaptive region matching for region-based image retrieval by
constructing region importance index. IET Comput. Vis. 8(2), 141–151 (2013).
27. Wang, C., B. Zhang, Z. Qin, and J. Xiong. Spatial weighting for bag-of-features
based image retrieval.InInternational Symposium on Integrated Uncertainty in
Knowledge Modelling and Decision Making. 2013. Springer..
28. X Tian, L Jiao, X Liu, X Zhang, Feature integration of EODH and Color-SIFT:
application to image retrieval based on codebook. Signal Process. Image
Commun. 29(4), 530–545 (2014).
29. Rashno, A., S. Sadri, and H. SadeghianNejad. An efficient content-based
image retrieval with ant colony optimization feature selection schema
based on wavelet and color features. in Artificial Intelligence and Signal
Processing (AISP), 2015 International Symposium on. 2015. IEEE.
30. M Rahimi, ME Moghaddam, A content-based image retrieval system based
on Color Ton Distribution descriptors. SIViP 9(3), 691–704 (2015).
31. C Yan, H Xie, D Yang, J Yin, Y Zhang, Q Dai, Supervised hash coding with
deep neural network for environment perception of intelligent vehicles.
IEEE Trans. Intell. Transp. Syst. 19(1), 284–295 (2018).
Mehmood et al. EURASIP Journal on Image and Video Processing (2018) 2018:48 Page 15 of 16
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
32. C Yan, H Xie, S Liu, J Yin, Y Zhang, Q Dai, Effective Uyghur language text
detection in complex background images for traffic prompt identification.
IEEE Trans. Intell. Transp. Syst. 19(1), 220–229 (2018).
33. C Yan, Y Zhang, J Xu, F Dai, J Zhang, Q Dai, F Wu, Efficient parallel
framework for HEVC motion estimation on many-core processors. IEEE
Trans. Circuits Syst. Video Technol. 24(12), 2077–2089 (2014).
34. C Yan, Y Zhang, J Xu, F Dai, L Li, Q Dai, F Wu, A highly parallel framework
for HEVC coding unit partitioning tree decision on many-core processors.
IEEE Signal Processing Lett. 21(5), 573–576 (2014).
35. Wang, Z., B. Fan, and F. Wu. Local intensity order pattern for feature description.
in Computer Vision (ICCV), 2011 IEEE International Conference on. 2011. IEEE.
36. Arthur, D. and S. Vassilvitskii. k-means++: The advantages of careful seeding.
In Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete
algorithms. 2007. Society for Industrial and Applied Mathematics.
37. J Shawe-Taylor, N Cristianini, Kernel Methods for Pattern Analysis (Cambridge
University Press, 2004).
38. R Short, K Fukunaga, The optimal distance measure for nearest neighbor
classification. IEEE Trans. Inf. Theory 27(5), 622–627 (1981).
39. Csurka, G., C. Dance, L. Fan, J. Willamowski, and C. Bray. Visual categorization
with bags of keypoints.InWorkshop on statistical learning in computer vision,
ECCV. 2004. Prague.
40. J Li, JZ Wang, Real-time computerized annotation of pictures. IEEE Trans.
Pattern Anal. Mach. Intell. 30(6), 985–1002 (2008).
41. SM Youssef, ICTEDCT-CBIR: Integrating curvelet transform with enhanced
dominant colors extraction and texture analysis for efficient content-based
image retrieval. Comput. Electr. Eng. 38(5), 1358–1376 (2012).
42. R Ashraf, K Bashir, A Irtaza, MT Mahmood, Content based image retrieval
using embedded neural networks with bandletized regions. Entropy 17(6),
3552–3580 (2015).
43. A Irtaza, MA Jaffar, Categorical image retrieval through genetically
optimized support vector machines (GOSVM) and hybrid texture features.
SIViP 9(7), 1503–1519 (2015).
44. GA Montazer, D Giveki, An improved radial basis function neural network
for object image retrieval. Neurocomputing 168, 221–233 (2015).
45. S Zeng, R Huang, H Wang, Z Kang, Image retrieval using spatiograms of colors
quantized by Gaussian Mixture Models. Neurocomputing 171,673–684 (2016).
46. Fei-Fei, L. and P. Perona. A bayesian hierarchical model for learning natural
scene categories. in Computer Vision and Pattern Recognition, 2005. CVPR
2005. IEEE Computer Society Conference on. 2005. IEEE.
47. E Yildizer, AM Balci, TN Jarada, R Alhajj, Integrating wavelets with clustering
and indexing for effective content-based image retrieval. Knowl.-Based Syst.
31,55–66 (2012).
48. E Yildizer, AM Balci, M Hassan, R Alhajj, Efficient content-based image
retrieval using multiple support vector machines ensemble. Expert Syst.
Appl. 39(3), 2385–2396 (2012a).
49. DNM Cardoso, DJ Muller, F Alexandre, LAP Neves, PMG Trevisani, GA Giraldi,
Iterative technique for content-based image retrieval using multiple SVM
ensembles. J Clerk Maxwell, A Treatise Electricity Magnetism 2,68–73 (2013).
50. Griffin, G., A. Holub, and P. Perona, Caltech-256 Object Category Dataset.2007.
51. N Ali, KB Bajwa, R Sablatnig, Z Mehmood, Image retrieval by addition of
spatial information based on histograms of triangular regions. Comput.
Electr. Eng. 54, 539–550 (2016).
52. Xiao, J., J. Hays, K.A. Ehinger, A. Oliva, and A. Torralba. Sun database: large-
scale scene recognition from abbey to zoo. in Computer vision and pattern
recognition (CVPR), 2010 IEEE conference on. 2010. IEEE.
53. G-H Liu, Z-Y Li, L Zhang, Y Xu, Image retrieval based on micro-structure
descriptor. Pattern Recogn. 44(9), 2123–2133 (2011).
54. C Carson, S Belongie, H Greenspan, J Malik, Blobworld: Image segmentation
using expectation-maximization and its application to image querying. IEEE
Trans. Pattern Anal. Mach. Intell. 24(8), 1026–1038 (2002).
55. SR Dubey, SK Singh, RK Singh, Rotation and scale invariant hybrid image
descriptor and retrieval. Comput. Electr. Eng. 46, 288–302 (2015).
Mehmood et al. EURASIP Journal on Image and Video Processing (2018) 2018:48 Page 16 of 16
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1.
2.
3.
4.
5.
6.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”), for small-
scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are maintained. By
accessing, sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of use (“Terms”). For these
purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or a personal
subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription
(to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the Creative Commons license used will
apply.
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data internally within
ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not
otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of companies unless we have your permission as
detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that Users may
not:
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to circumvent access
control;
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil liability, or is
otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by Springer Nature in
writing;
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer Nature journal
content.
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates revenue,
royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain. Springer Nature journal
content cannot be used for inter-library loans and librarians may not upload Springer Nature journal content on a large scale into their, or any
other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not obligated to publish any information or
content on this website and may remove it or features or functionality at our sole discretion, at any time with or without notice. Springer Nature
may revoke this licence to you at any time and remove access to any copies of the Springer Nature journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express or implied
with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or warranties imposed by law,
including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be licensed
from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other manner not
expressly permitted by these Terms, please contact Springer Nature at
onlineservice@springernature.com
Content uploaded by Zahid Mehmood
Author content
All content in this area was uploaded by Zahid Mehmood on Jun 14, 2018
Content may be subject to copyright.
