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CCTV object detection with fuzzy classification and image

enhancement

Andrzej Matiolański

1

&Aleksandra Maksimova

2

&

Andrzej Dziech

1

Received: 1 October 2014 /Revised: 20 April 2015 / Accepted: 18 May 2015

#The Author(s) 2015. This article is published with open access at Springerlink.com

Abstract In this paper we propose a novel approach for pattern recognition problems with

non-uniform classes of images. The main concept of this classification method is to describe

classes of images with their fuzzy portraits. This approach is a good generalization of the

algorithm. The fuzzy set is calculated as a preliminary result of the algorithm before the final

decision or rejection that solves the problem of uncertainty at the boundaries of classes. We use

the method to solve the problem of knife detection in still images. The main aim of this paper

is to test fuzzy classification with feature vectors in a real environment. We used selected

MPEG-7 descriptor schemes as feature vectors. The method was experimentally validated on a

dataset of over 12,000 images. The article describes the results of six experiments which

confirm the accuracy of our method. In addition we conducted a test with enhanced images,

achieved with two state-of-the-art super-resolution algorithms.

Keywords Pattern recognition .Fuzzy classifier .Fuzzy inference .Data analysis .Knife

detection .Feature descriptor .Image enhancement

1 Introduction

The concept of automated image understanding is common in public safety applications and it

has been explored extensively in many domains. It is an active research topic not only in the

computer vision domain [16]. The next step is the detection of dangerous situations based on

Multimed Tools Appl

DOI 10.1007/s11042-015-2697-z

*Andrzej Matiolański

matiolanski@kt.agh.edu.pl

Aleksandra Maksimova

maximova.alexandra@mail.ru

Andrzej Dziech

dziech@kt.agh.edu.pl

1

Department of Telecommunication, AGH University of Science and Technology, Kraków, Poland

2

Institute of Applied Mathematics and Mechanics, National Academy of Science of Ukraine, Donetsk,

Ukraine

recordings from IP surveillance cameras. This paper deals with analyzing video footage

obtained using CCTV systems. There are various problems associated with analyzing poten-

tially dangerous situations. A knife held in the human hand is an example of a signal of danger.

Such scenes are generally dynamic and quick. Our aim is to solve the problem of knife

recognition in frames from camera video sequences.

There are several known approaches to knife detection. Żywicki et al. proposed a method

based on a simple wavelet classifier using Haar cascades [21]. Kmiećet al. presented an

algorithm involving the active appearance model (AAM) [8,9]. The AAM takes into account

the sharpness of the blade, and detects corners in images containing knives. The final results of

the method were presented in [4] on a small dataset. Maksimova used the geometrical

approach in her study published in [14]. The methods work with images pixel by pixel, which

is inefficient in many cases. The approach of representing the image as a set of feature vectors

using MPEG-7 descriptors is introduced in this paper.

In prior studies, an adequate algorithm accuracy was only achieved for simple examples

when the knife is clearly visible in the image. For more difficult situations, when the blade

reflects light reducing its visibility or the knife is turned edgewise to the frame, the quality of

the algorithm is poor. We used a single frame from the sequence to achieve conditions

approaching reality in which only some frames are of sufficiently high quality. Finally, we

tested the algorithm with artificially enhanced images from CCTV footage.

Methods of object identification in images are distinguished by high numbers of false

positives. Quality can be estimated more effectively by multi-valued truth-space used in fuzzy

logic theory [11]. In such case, the result of the classification algorithm is an information

vector with a degree of confidence for the object assigned to a particular class. Methods of

pattern recognition that use fuzzy sets are known as fuzzy classifiers [12]. Approaches to

creating fuzzy classifiers include tuning knowledge databases using evolutionary methods [6],

applying FuzzyLVQ (fuzzy learning vector quantization) and FSOM (fuzzy self-organizing

map) networks [3], and using fuzzy clustering methods [2]. We use the fuzzy clustering

approach extended for the pattern recognition problem.

The paper is organized as follows: Section 2 describes the feature vectors, Section 3

introduces the inference model based on the fuzzy classification method, Section 4 contains

experimental verification of the approach, and Section 5 is the conclusion.

2 MPEG-7 feature vectors

Cropped images were obtained from CCTV camera footage. They were scanned with a sliding

window of size W×H, so we solved the problem for these W×Hfragments of original images.

We treated the problem as a pattern recognition one. The database consists of two classes of

images: positive examples (PE) if the image features a knife (Fig. 1a), and negative examples

(NE) in all other cases (Fig. 1b). The images were taken indoors or through car windows,

since carrying knives in public is illegal in Poland.

Current literature describes many different visual descriptors with their advantages and

disadvantages [1]. We used visual descriptors from the MPEG-7 standard. Because of the

issues specific to recognizing knives in images, we chose two descriptors: edge histogram [19]

and homogeneous texture [18]. The former, containing information about various types of

edges in the image, is a numerical vector comprising 80 types of edges. The latter describes

specific image patterns: directionality, coarseness and regularity. The two descriptors provide

Multimed Tools Appl

us with information about features characteristic of knives. We avoid using color and shape

descriptors because of light reflections and the high number of knife types. Descriptors based

on keypoint matching (such as SIFT or SURF) also do not provide good results. The majority

of keypoints are detected in the background part of the image rather than in the knife itself. The

MPEG-7 feature vectors described are used to build the model presented in this work.

3 Fuzzy classification model

To create a model for knife detection, we considered the specifics of the problem and the

presentation of images using MPEG-7. Let us discuss solving the pattern recognition problem

in the face of uncertainty [20], where a real-world object (e ∈O) is represented as a vector of

informative features:

x¼x1;x2;…;xm

ðÞ;ð1Þ

where x

i

=f

i

(e), f

i

is the method for measuring the i-th feature of the object:

fi:O→Xi;ð2Þ

where X

i

is the assumed region for the feature, due to the nature of the object and its

measurement method, X

i

⊂ℛ,whereℛis a set of real numbers.

Let Ωbe an alphabet of classes of images for the pattern recognition problem:

Ω¼ωj

k

j¼1ð3Þ

where ω

j

is the name of the class of images, jis the element index, and kis their number.

A finite set of samples is known:

Z¼ei;oi

n

i¼1ð4Þ

where e

i

is the real object, described by the feature vector x(1), iis the element index from the

set, n is the number of samples, and o

i

∈Ωis its label.

a) b)

Fig. 1 Example images: apositive example, bnegative example

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Let us construct a classifier as a mapping:

D:X↦e

Ωð5Þ

where Ωis an alphabet of classes of images (3), e

Ωis t he set of fuzzy subsets over the alphabet

of classes, and X=X

1

×X

2

×…×X

m

is the region of admissible values in the feature vector space

of object x, specified in (1). The classification result in this situation will be the fuzzy set:

e

α¼Xk

i¼1αi=ωið6Þ

where α

i

is the degree of similarity between the object xand class of images ω

i

. To improve the

method, the final decision about an object belonging to a given class of images is performed by

analyzing the fuzzy set e

α,specified in (6).

3.1 The clustering algorithm with an unknown number of classes

We propose to carry out a preliminary analysis of the data in order to establish the intra-

structure for each class of images. We use the FCM-fuzzy (fuzzy C-means) clustering

algorithm [2]. The result of the algorithm is a fuzzy c−partition as matrix U=[u

ki

]

n×c

,where

u

ki

–is the degree of membership x

k

to cluster i,n–is the number of objects x,andс–is the

number of clusters, which is a parameter of the algorithm. Two types of с-partition are used in

the work:

Mfcn ¼U∀kXc

i¼1uki ¼1

no

;ð7Þ

Mhcn ¼U∈Mfcn

∀k;iu

ki∈0;1

fg

noð8Þ

where M

fcn

–is a fuzzy partition and M

hcn

–is a crisp c-partition.

Aside from the c–partition U∈M

fcn

, the results of the algorithm are geometrical centers of

clusters G={g

i

,g

2

,…,g

c

}⊂ℛ

m

. The FCM algorithm minimizes the Bezdek-Dann functional:

JFCM

γU;G;XðÞ¼

Xn

k¼1Xc

i¼1uγ

kid2xk;gi

ðÞ→min

|{z}

U;G

fg ð9Þ

under the constraints:

Xc

i¼1uki ¼1;∀xk;k¼1;n;ð10Þ

where γ–fuzziness coefficient, and d

2

(x,g)–square of the distance between the element xand

the center of the cluster g. Here, Euclidean distance is used. The altering-optimization method

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is used in the FCM algorithm. It is calculated at each step by the centers of the cluster

membership degrees u

ki

for object x

k

:

uki ¼Xc

j¼1

dxk;gi

ðÞ

dxk;gj

0

@1

A

2

γ−1

0

B

@1

C

A

−1

;ð11Þ

where 1≤i≤c,1≤k≤n, and then new centers of clusters by u

ki

:

gk¼Xn

k¼1uγ

ki xk

Xn

k¼1uγ

ki ð12Þ

To start with, the algorithm is used to determine the initial values of cluster prototypes.

Minimal p1fiand maximal p2fivalues are calculated for every feature f

i

,i¼1;m, specified in

(2) by samples Z, specified in (4):

gk¼p1þkp2−p1

ðÞ

cþ1

ðÞ

;ð13Þ

where p1¼p1f1;p1f2;…;p1fm

,andp2¼p2f1;p2f2;…;p2fm

,1≤k≤c,c–the number

of clusters.

The main disadvantage of the FCM algorithm is the requirement to set as a parameter of the

algorithm the number of clusters that in the study of data structures is unknown in advance. To

solve the problem of finding the optimal number of clusters, the criterion of cluster adequacy is

used. Informally, it can be described as the most appropriate cluster structure, which is different

for each task. It also implies that the choice of criteria will be different.

Certain criteria of cluster validity exist, with the following considered for the proposed

fuzzy model. Bezdek’s partition coefficient is [18]:

vPC UðÞ¼

Xn

k¼1Xc

i¼1u2

ki

n;ð14Þ

where U–fuzzy с-partition, с–the number of clusters, and n–the number of samples

elements. The next property is v

PC

:

vPC ¼1⇔U∈Mhcn;ð15Þ

vPC ¼1

c⇔U¼1

c

¼U;ð16Þ

where M

hcn

is specified in (8), and ¯

U–is the fuzziest partition available, since it assigns every

point in Xwith equal membership values 1

cto all cclasses. Bezdek’s partition coefficient

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belongs to the class that uses information about Uonly, but not information about the data

itself, such as Gand X.

The Xie-Beni index v

XB

[18] belongs to the class of validity criteria using the total

information (U,G;X):

vXB U;G;XðÞ¼

Xc

i¼1Xn

k¼1u2

kijjxk−gijj2

nðmin

|{z}

i≠jjjgi−gjjj2

no

Þ¼

σ

n

sep GðÞ

2

43

5;ð17Þ

where σ−is the ratio of the total variation of (U,G), and the separation sep(G)ofthe

vectors G:

σU;G;XðÞ¼

Xc

i¼1Xn

k¼1u2

kijjxk−gijj2

;

sep GðÞ¼min

|{z}

i≠jjjgi−gjjj2

no

:ð18Þ

The lower value v

XB

specified in (17) indicates a better partition on X, which is right for γ=

2[17]. Studies of the influence of the fuzziness coefficient on the Xi-Beni index point to its

instability for high values of γ[17].

To find the optimal number of classes, a method based on the scheme presented in

Fig. 2is proposed in [15]. The result is presented as a fuzzy model FP

ω

=<U,G,X,c>

which will be referred to as a fuzzy portrait of a class of images [7] within the

general concept proposed by the author, where ωis the class of images from alphabet Ω,

specifiedin(3).

Fig. 2 Generalschemeoffuzzymodelcreation

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In the scheme, X

ω

⊂ℛ

m

corresponds to the part of input data Z, specified in (4), for

elements with o

i

=ω;Ν(X

ω

):ℛ

m

↦[0,1]

m

is the normalization procedure for the sample; C

FCM

is the clustering algorithm presented as function C

FCM

(X;c,…):ℛ

n×m

↦M

fcn

,wherenis the

number of sample elements, M

fcn

is specified in (7), and сis the parameter of the algorithm that

specifies the number of clusters and other options of the FCM algorithm [2,17]; v*isthe

validity criterion such as (14) or (17); and U1¼1;1;…;1

|ﬄﬄﬄﬄﬄ{zﬄﬄﬄﬄﬄ}n

T

is the unit vector G

1

={g

1

},

where g

1

is calculated by (12) providing that U=U

1

. The range of values is с=2,3,…,

c

max

, with cmax ≤ﬃﬃﬃ

n

p,wherenis the cardinality of the set X

ω

. The validity criteria are

analyzed during the fuzzy model creation step. For criterion (17) the best partition is

at the minimal value.

The proposed scheme of intra-class analysis and fuzzy model FP

ω

creation will be used as

part of the classification method (5). It is the set of samples Z(4) whose elements are

grouped by membership to classes of Ωsuch as Xωi¼x1;x2;…;xm

ðÞjx1x2;…;xm;ωi

ðÞ∈Z

fg

,

∀i¼1;K. For every class of images a local fuzzy model is created. The method to combine all

local models into a single fuzzy model is:

FM ¼<FPωi

fg

K

i¼1;D>; ð19Þ

where Dis the decision making algorithm (5). Next, we define D and show how the problem of

pattern recognition is solved under uncertainty using only the information from the model (19).

3.2 Decision making method

As a result of algorithm Dfor object xunder conditions of uncertainty is made. According to

the statement of the problem considered is a fuzzy set e

α∈e

Ω, specified in (6).

The main idea for this method is as follows. The tuning of the classification model is carried

out for each class of images separately; however, decision making uses combined data for all

classes of images. The advantages of this approach are the transparency of the model and the

adaptability of the method to new types of data. It will be considered as the method for

decision making Dx

*;FM;Λ;ε

¼e

α,whereFM is the model (19), e

αis the formula (6),

Λ¼p1fi;p2fi

no

m

i¼1is the set of pairs including the minimal p1fiand maximal p2fivalues,

used for normalization of data, f

i

is the feature of the object, i¼1;m,mis the number of

features, εis the threshold value, Kis the number of classes, and x* is the new recognizable

object. Let us denote the set of cluster centers corresponding to model FPωias Gωiand their

number as cωi.

Step 1 Transform x* using Λwith formula:

x¼x*−p1

p2−p1

;ð20Þ

where p

1

and p

2

as in formula (13).

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Step 2 Calculate α

i

for every ωi;i¼1;Kto have e

αin formula (6). Set ω=ω

i

,α=α

i

,FP ¼FPωi.

Calculate the distance from xto the nearest center of cluster for class ωby model FP:

l¼min g∈Gωdx;gðÞðÞ;ð21Þ

where dis the distance.

Determine B

ω

, which consists of the centers of whole clusters of the model

except class ω:

Bω¼[k

j¼1

j≠i

Gωk;ð22Þ

where iis the index of class ω.Calculateαwith the formula:

α¼1þXg∈Bω

l2

d2x;gðÞ

−1

;ð23Þ

where lis calculated by (21), B

ω

is determined in (22), and dis the distance.

The formula is the result ~

α̃from formula (6) from α(23) for all classes.

Step 3 Change ~

α̃by excluding classes with a low membership α

i

replacing it with the formula:

αi¼0;if α<ε

1;if α≥ε

;ð24Þ

where εis the parameter of the classification method corresponding to the threshold.

Step 4 Calculate the resulting class of images:

Hðe

αÞ¼ ωi;if ∃!i:αi¼1;

ω0;otherwise:

;ð25Þ

where ω

0

is an unknown class of images, affiliation to which denotes the rejection option.

Fig. 3 Class histograms in the fuzzy classification model for the HT descriptor during the decision making

process. The algorithm cannot determine the classes

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4 Experimental verification

We developed standalone computer software as a practical implementation of the method

proposed in the article. The specially designed library for fuzzy classification was developed in

C++ and used in this application to create a classification algorithm D, specified as in (5). The

application supports learning and test modes of operation. Two sets of samples are used in the

experiments:

–learning samples used to create the model in learning mode,

–test samples used to estimate the quality of classification in test mode.

A dataset [10] of 12,899 examples of images, including 9340 NE and 3559 PE,

was prepared. The alphabet of classes of images is Ω={NE,PE}. The edge histogram

(EH) and homogeneous textures (HT) descriptors were calculated for the whole

dataset.

We estimated the classification ability of the descriptors using a pattern recognition

method based on histograms [13]. The histograms form a part of the fuzzy classifi-

cation model (described in detail in Section 3). Example histograms of four features

in FCM model are presented for the HT descriptor in Fig. 3and for the EH descriptor

in Fig. 4. The red line corresponds to PE and the green line corresponds to NE; the

range of permissible values of features follows the axis of abscissa, and the normal-

ized degree of membership follows the ordinate. The example features originate from

our fuzzy classification model. Statistical analysis of the set of samples presented with

the HT descriptor shows that this descriptor cannot be used to distinguish classes of

images (Fig. 3). The classification was carried out with the EH descriptor since it

provides good results of statistical analysis of the images (Fig. 4). A comparison of

Fig. 4 Class histograms in the fuzzy classification model for the EH descriptor during the decision making

process. Classes are clearly visible and found by the algorithm

Tab l e 1 Confusion matrix

Method model ω

0

ω

1

ω

2

ω

1

q

10

q

11

q

12

ω

2

q

20

q

21

q

22

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Figs. 3and 4clearly shows the differences between the two descriptors. HT is not

able to provide visible classes. As a result, the algorithm based on the fuzzy

classification model frequently returns no answers, since it does not have sufficient

data to make a decision. The same problem does not apply to EH, and the classes are

easily determined as separate pieces of data. This shows that the EH descriptor is

suitable for addressing the problem described in this paper, in contrast to HT.

Following analysis of all such histograms, we excluded the HT descriptor from further

consideration. In the remainder of the article, we present the results of experiments for

the EH descriptor which demonstrate the high quality of the algorithm.

Let us consider the confusion matrix Qto evaluate the quality of algorithm D.Cellq

ij

of the

confusion matrix contains the number of elements from the test sample for which ω

i

is the right

class; the classification result is ω

j

.Table1shows an example of the confusion matrix for the

problem with two classes. An artificial class ω

0

is used as the reject option, as in step 4 of the

decision making algorithm.

Let p

i

be the a priori probability for class ω

i

,i¼1;K,∑K

i¼1pi¼1. To calculate the

probability of an event, the algorithm classifies the object belonging to class ω

i

as an object

of class ω

j

,i¼1;K;j¼0;Kusing the formula:

pij ¼qij

XK

k¼0qik

;ð26Þ

where q

ij

is an element of the confusion matrix, and Kis the number of classes.

a) b) c) d)

Fig. 5 Example images: agood positive example, bbad positive example, cgood negative example, dbad

negative example

Tab l e 2 Learning and test sets

Exp. 1 Exp. 2 Exp. 3 Exp. 4 Exp. 5

Num Desc. Num Desc. Num Desc. Num Desc. Num Desc.

Learning samples PE 378 Good 378 Good 573 Good 573 Good 2348 All

NE 6164 All 284 All 6164 All 431 Good 6164 All

Test samples PE 195 Good 195 Good 3026 All 3026 All 1211 All

NE 3176 All 147 All 3176 All 8909 All 3176 All

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Two types of error for the algorithm with the reject function are considered. The first type of

error is calculated as:

AI¼XK

i¼1piXK

j¼1

j≠i

pij

0

B

@1

C

A;ð27Þ

where p

i

is the probability for class ω

i

and p

ij

is calculated by (26). To calculate the probability

of an event:

R¼XK

i¼1pipi0;ð28Þ

where p

i

,p

i0

are as in (13). The second type of error includes the first type of error and a

rejection, and is calculated as:

AII ¼AIþRð29Þ

We conducted a series of experiments on the prepared set of samples. The dataset contains

examples of varying quality. The quality of negative examples has a lower impact on the

results (26–29). For positive images, poor quality has a significant impact on the results.

Tab l e 3 Experiment results

Knife detection Exp1 Exp2 Exp3 Exp4 Exp5

Learn A

I

27.74 % 25.47 % 14.30%13,90% 17.16 %

R1.23 % 1.53 % 0.49 % 0.53 % 0.87 %

A

II

26.50 % 27.00 % 14.78 % 14.42 % 17.92 %

Tes t A

I

27.02 % 26.76 % 14.32%14.59% 17.46 %

R1.53 % 0.70 % 0.64 % 0.66 % 0.84 %

A

II

28.56 % 27.46 % 14.96 % 15.25 % 18.30 %

a) b)

Fig. 6 Resized images: awithout quality enhancement, bwith quality enhancement

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Each image was analyzed by an operator in order to select good examples. As a result, 573

good positive examples and 431 good negative examples were selected. Figure 5shows some

example images.

Five sets of samples were selected for further tests. Their numerical descriptions are

presentedinTable2. Examples are marked as ‘good’if the selected examples are used, and

‘all’if all available examples are used. For every example, the set of samples is divided at a

ratio of two to one.

Results of experiments in Table 3show that the best characteristic has fuzzy models created

on sets which contain good positive examples only. This is experiments 3 and 4, with 14 % of

errors on test samples. This shows that the model should be trained on good positive examples

only. The quality of negative examples has a lower impact on the result.

When compared to the state-of-the-art algorithm, our results are satisfactory. We conducted

additional experiments to check the accuracy of other methods on the same dataset. The

algorithm presented by Żywicki et al. returns 29 % of errors, while the algorithm presented by

Masimova et al. returns 23 % of errors. Results presented by Kmiećet al. are impressive (92 %

accuracy on 40 images); however, the best outcome achieved on our dataset was 21 % of

errors.

In addition, we conducted some experiments with super-resolution techniques. We created a

new dataset with enhanced images from the original database of knife images. We used two

known algorithms for a single image at super-resolution. The first is a learning-based method

introduced by Kim and Kwon [7], while the second is a method based on iterative Weiner

filtering originally presented by Hung and Siu [5]. During initial tests, we were not able to train

Kim’s algorithm, therefore we did not achieve a similar (or close to similar) outcome with our

dataset in terms of image quality. The second algorithm gave an outcome similar to that

reported in the original paper. The average PSNR achieved for the image database was 0.41 dB

lower. We assume that an appropriate level of image enhancement was achieved. Figure 6

Tab l e 4 Learning and test sets for the experiment with enhanced images

Exp.6

Num Desc.

Learning samples PE 2348 All

NE 6164 All

Test samples PE 1211 All

NE 3176 All

Tab l e 5 Results for the experiment with enhanced images

Knife detection Exp6 Exp5

Learning A

I

19.74% 17.16 %

R1.01 % 0.87 %

A

II

20.71 % 17.92 %

Tes t A

I

20.47% 17.46 %

R0.91 % 0.84 %

A

II

21.07 % 18.30 %

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shows enhanced and non-enhanced images from the dataset, with the improved quality clearly

visible. The image looks smooth and does not show distortions near the edges. All images

from the dataset were enlarged by a magnitude of two (from a resolution of 100×100 pixels to

200× 200 pixels).

The next step was to apply our method to a new, high resolution (HR) dataset. Experiment 6

was conducted using the HR database under the same conditions as experiment 5 (Table 4).

This allowed us to directly compare results from both experiments. A further experiment was

conducted on the entire dataset, since it was difficult to determine which images qualify as

‘good’following enhancement.

Results of experiment 6 are shown in Table 5. The experiments were conducted with a

cross-validation technique to avoid significant distortions. The results achieved in experiment

6 are worse than those from experiment 5, which means that image enhancement does not

improve classification accuracy. However, the images appear to be visually better to the human

eye, while those treated with the algorithm do not. This is due to the EH descriptors, since the

enhancement process smooths the image and some information is lost from near the edges. EH

descriptors contain less data, therefore the classification algorithm cannot provide results with

the same or higher level of accuracy (Table 6).

5 Conclusions

The article presents a fuzzy model for knife detection. Selected elements of the MPEG-7

descriptor are used as a feature for the pattern recognition problem. Experimental verification

shows that the method can be successfully used in difficult situations. The result of less than

15 % of images being mislabeled for the entire dataset is good for the problem at hand,

although it would need to be improved for real systems. The method can be used simulta-

neously with other image processing techniques. Further research will be aimed at assessing a

set of sequential images from video, and using a combination of our method with other

approaches.

The approach can be used for different pattern recognition problems with non-uniform

classes where the object has a specific form, such as the knife in our example. However, image

descriptors should be selected according to the features of the objects under consideration. The

fuzzy model must be trained using good examples only. Subsequently, such a model can be

applied to examples with less clear images. We also intend to generalize our algorithm to solve

the detection problem for a wide range of objects in an automatic way.

A further conclusion concerns processing images following quality enhancement. The

experiment shows that in some cases images should be analyzed before any manipulation is

applied, even if enhancement improves the image quality to the human eye (e.g., CCTV camera

operator). This is significant in creating architectures for modern, intelligent CCTV systems.

Tab l e 6 Results comparison for state-of-the-art and proposed algorithms

Algorithm Żywicki [21]Kmieć[4] Maksimova [14]Proposed Proposed (with image

enhancement)

Error rate (achieved

on dataset [10])

29 % 21 % 23 % 14 % 20 %

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Acknowledgments This research has been financed by the European Regional Development Fund under the

Innovative Economy Operational Programme, INSIGMA project No. POIG.01.01.02-00-062/09.

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 repro-

duction 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.

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Multimed Tools Appl

M. Sc. Eng Andrzej Matiolanski He is a Ph.D. student and Teaching Assistant at the Department of

Telecommunications of AGH University of Science and Technology. He has received his M.Sc. degree from

the Faculty of Physics and Applied Computer Science of AGH University of Science and Technology in 2010.

His research interests include computer vision, image processing and super-resolution algorithms. He has actively

participated in both national and international research projects like INDECT, INSIGMA, MAYDAY EURO

2012 and IMCOP. Email: matiolanski@kt.agh.edu.pl

Aleksandra Maksimova received MSc degree (2004) of Computer Science from the Donetsk National

University and PhD of Information technology (2014) in Ukraine. Currently she is researcher in the Institute

of Applied Mathematics and Mechanics of National Academy of Science of Ukraine. Primary research interests

are pattern recognition, data mining, fuzzy theory, fuzzy classification methods.

Multimed Tools Appl

Andrzej Dziech holds the position of a full professor at the Department of Telecommunications of AGH

University of Science and Technology in Krakow, Poland. He received his M.Sc. and Ph.D. degrees from the

Institute of Electrical Engineering in Saint Petersburg in 1970 and 1973, respectively, and the D.Sc. from

Technical University of Poznan in 1978. He is an author of 6 books and nearly 180 publications. He was a

supervisor of 18 Ph.D. students. His fields of interest are related to digital communication, image and data

processing, data compression, information and coding theory, random signals, computer communications

networks and signal processing. He was awarded 4 times for research achievements by the Ministry of Education

of Poland. Professor Dziech actively participated in numerous international research projects, e.g., Tempus,

Knixmas, Calibrate. Currently, he is coordinating a European Union FP7 integrated project INDECT.

Multimed Tools Appl