Int. Journal of Electrical & Electronics Engg. Vol. 2, Spl. Issue 1 (2015) e-ISSN: 1694-2310 | p-ISSN: 1694-2426 NITTTR, Chandigarh EDIT -2015 116 Query Formation Image Database Visual Content Visual Content Feature Vectors Feature Database Relevance Feedback Similarity Comparison Indexing & Retrieval Retrieval results Output A Review of Feature Extraction Techniques for CBIR based on SVM Navneet Kaur 1 , Sonika Jindal 2 1 M.Tech, Department of Computer Science and Engineering 2 Assistant Professor, Department of Computer Science and Engineering Shaheed Bhagat Singh College of Engineering and Technology, Ferozepur [email protected] & [email protected]Abstract: As with the advancement of multimedia technologies, users are not gratified with the conventional retrieval system techniques. So a application “Content Based Image Retrieval System” is introduced. CBIR is the application to retrieve the images or to search the digital images from the large database .The term “content” deals with the colour, shape, texture and all the information which is extracted from the image itself. This paper reviews the CBIR system which uses SVM classifier based algorithms for feature extraction phase. Index Terms— Content Based Image Retrieval , Support Vector Machine , Feature Extraction, Relevance Feedback. I. INTRODUCTION 1 Content Based Image retrieval is the method to retrieve images based on various derived features such as colour, texture and shape[1]. According to the users requirement, this technique basically use visual contents to search images from the large databases. Early techniques were based on textual annotation of images rather than the visual contents [2]. In other terms, firstly images are annotated with text, and then search can be done using text based method from traditional database management system. The first and foremost retrieval approach based on combination of textual data into each image and retrieve those images by keywords which is the traditional database query technique which is time consuming and too much gruelling task. In CBIR system the images are extracted form the database on the basis of visual contents and represented as feature vectors. Furthermore, SVM is the classifier which is basically for the regression and classification on the basis of various tools and techniques. Various algorithms are used to extract the features of images by using the SVM classifier. Moreover learning techniques are used it may be supervised or unsupervised learning techniques which are based on the training and testing phases. Firstly retrieve the feature vectors from the images (features can be colour, texture and shape) and then keep feature vectors into different databases for the future purpose. The two images in the database is similar to the query image only when the distance between two images feature vectors is small. Figure 1 shows the various phases of CBIR. Figure 1: Content Based Image Retrieval II. FEATURE EXTRACTION Feature extraction for CBIR is the method of computing the attributes of various digital images which can be used to define information regarding the contents of the image. A feature can be associated with the single attribute or composite description of distinguished attributes. The classification of features is general purpose or domain dependent. The general purpose features can be designed anywhere in the context whereas domain dependent features are used for a specific application[6]. The advantage for feature extraction is to detect the different types of features which are used in images[9].There has been huge work done on various approaches to detect the different features among images. Feature extraction will be solicited very customarily; therefore, it would be exact, accurate and time efficient. The dimension of the vector can be reduced by using the feature extraction techniques on the basis of: Colour Texture Shape COLOR Color is more considerable visual content for the retrieval of images. It reveals the most broadly used feature in the CBIR system. The preference for the selection of features of the colour, based on results of the segmentation. For illustration, if homogeneous colour is not provided by the segmentation method then obviously this is not a better choice[4]. Firstly to represent the colour images, colour space is used. Typically, the summation of red, green and blue gray level intensities is represented by the gray level intensities which represent the RGB space.RGB space mainly used for image display in colour space. This
Abstract: As with the advancement of multimediatechnologies, users are not gratified with the conventionalretrieval system techniques. So a application “Content BasedImage Retrieval System” is introduced. CBIR is theapplication to retrieve the images or to search the digitalimages from the large database .The term “content” dealswith the colour, shape, texture and all the information whichis extracted from the image itself. This paper reviews theCBIR system which uses SVM classifier based algorithms forfeature extraction phase.
Index Terms— Content Based Image Retrieval , SupportVector Machine , Feature Extraction, Relevance Feedback.
I. INTRODUCTION1
Content Based Image retrieval is the method to retrieveimages based on various derived features such as colour,texture and shape[1]. According to the users requirement,this technique basically use visual contents to searchimages from the large databases. Early techniques werebased on textual annotation of images rather than the visualcontents [2]. In other terms, firstly images are annotatedwith text, and then search can be done using text basedmethod from traditional database management system. Thefirst and foremost retrieval approach based on combinationof textual data into each image and retrieve those imagesby keywords which is the traditional database querytechnique which is time consuming and too much gruellingtask. In CBIR system the images are extracted form thedatabase on the basis of visual contents and represented asfeature vectors. Furthermore, SVM is the classifier whichis basically for the regression and classification on thebasis of various tools and techniques. Various algorithmsare used to extract the features of images by using theSVM classifier. Moreover learning techniques are used itmay be supervised or unsupervised learning techniqueswhich are based on the training and testing phases.Firstly retrieve the feature vectors from the images(features can be colour, texture and shape) and then keepfeature vectors into different databases for the futurepurpose. The two images in the database is similar to thequery image only when the distance between two imagesfeature vectors is small. Figure 1 shows the various phasesof CBIR.
Figure 1: Content Based Image Retrieval
II. FEATURE EXTRACTION
Feature extraction for CBIR is the method of computingthe attributes of various digital images which can be usedto define information regarding the contents of the image.A feature can be associated with the single attribute orcomposite description of distinguished attributes. Theclassification of features is general purpose or domaindependent. The general purpose features can be designedanywhere in the context whereas domain dependentfeatures are used for a specific application[6]. Theadvantage for feature extraction is to detect the differenttypes of features which are used in images[9].There hasbeen huge work done on various approaches to detect thedifferent features among images. Feature extraction will besolicited very customarily; therefore, it would be exact,accurate and time efficient. The dimension of the vectorcan be reduced by using the feature extraction techniqueson the basis of:
ColourTextureShape
COLOR
Color is more considerable visual content for the retrievalof images. It reveals the most broadly used feature in theCBIR system. The preference for the selection of featuresof the colour, based on results of the segmentation. Forillustration, if homogeneous colour is not provided by thesegmentation method then obviously this is not a betterchoice[4]. Firstly to represent the colour images, colourspace is used. Typically, the summation of red, green andblue gray level intensities is represented by the gray levelintensities which represent the RGB space.RGB spacemainly used for image display in colour space. This
includes three colours components red, green and blue alsoknown as ‘additive primaries’[4]. In contrast, for thepurpose of printing CMY color space s used whose colorcomponents are cyan, magenta and yellow also known as‘Subtractive Primaries’. Light absorption is produced colorin CMY space. Color features have various advantages:RobustnessEffectivenessImplementation SimplicityComputational SimplicityLow Storage Requirements
TEXTURE
Texture is one more another significant property of theimages. It deals with the visual patterns which have thequality of uniformity or settlement that do not consequencefrom only the presence of single intensity. For the purposeof both computer vision and pattern recognition, differenttexture representations have been explored.Texture representation can be divided into various classes:Structural methods:These methods include the graphs and morphologicaloperators and their rules. This reveals with the action ofimage primitives and presence of parallel objects. Thestructural method introduces to retrieve the structuralinformation under the assumption of human visualperception[4]. The main target image quality can be furthersubdivided on the basis of original image that is distortionfree and another is distorted image. If reference of image isknown that is called to be full reference otherwise noreference or it can be blind quality approach is obtained.Moreover, another method introduces the reference imageis available partially which consist of set of extractedfeatures for evaluating the quality of distorted image.
Statistical methodsThese methods consist of famous co-occurrence matrix,Fourier power spectra, Shift invariant principal componentanalysis (SPCA), Tamura feature, Multi-resolution filteringtechnique such as Gabor and wavelet transform,characterize the texture by statistical distribution of theimage intensity.
SHAPE
Shape representation can be subdivided into two categories
Boundary based which includes the outer boundary of theshape only. This is completed by describing the regionwhich uses only the external features, such as the pixelsalong with the object boundary.
Region Based is completely different from the boundarybased. This can be used the whole shape region byexplaining the internal characteristics such as the pixelspresent in the region.
III.SUPPORT VECTOR MACHINE
SVM is the state of art classification method which isintroduced in 1992 by Bose, guy on and Vapnik.SVM is anbest tool for regression and classification.[16] Supportvector machine may be defined as this is the linear
function of the high dimensionality feature space whichconsist of the postulate space. SVM is a most beneficialtechnique for the data classification. Sometimes unsatisfiedresults are obtained using the neural networks and even itis easy to use. The classification task includes the trainingand testing data which consist the same data instances.Each sample in the training set consist the target valuesand its various attributes.[17] The major goal of SVM is tojudge that the target value of various data instances in thetesting set which are given only the attributes of the data.Classification in the SVM is the instance of supervisedlearning. Now discussion of various algorithms on whichSVM is used.Improved SVM also known as the SVM clustering.Clustering is an unsupervised learning technique whichsimply means the decomposition of objects into variousclusters and subgroups on the basis of similarity.
SVM with Gabor magnitude
Gabor filters are the combination of wavelets, whereindividual wavelet which captures the energy at specificdirection and frequency. For the detection of differentorientation and frequencies, the Gabor filter banks aredesigned[17]. A hybrid approach to CBIR is used, SVM istrained and then therefore the database of images is labeledusing feedback from users which consider relevant andnon-relevant. Standard deviation of Gabor can becalculated to obtain the Gabor feature vector[17]. Varioussteps are included to apply the Gabor algorithm:Divide the whole image into 16*16 sub-blocks.Calculate for four different scales at eight different angles,which will give eight different angles at one particularscale.Calculate standard deviation and mean, which gives theGabor feature vector.
Image Retrieval using SVM and SURF
Surf (Speeded up robust Features) is a local featuredetector; first introduced by Herbert Bay et al in 2006which is magnificent by SIFT descriptor.Basically SURF is the combination of 2D-Haar waveletwhich makes logical use of intrinsic images[18]. Using thehistogram of gradient orientation, construct the descriptorvector of length 64[21]. Only CBIR with Surf and SVMmethod does not provide the better results, so that is why,use the CBIR with Surf ,Artificial Neural Network andSVM gives the improved results. The combination ofmodified SURF, Similarity matching algorithms and imageblending algorithm makes the prospective image system.Load the image as an input.Pre-process (Convert to grey scale, binary form).Extract the features using Image Histogram.Matching and recognition using SURF feature, SVM andNN.Display the results and obtained the average accuracy
SVM with Quadratic Distance Metric
For the extraction of colour features Global Colourhistogram is used. There was an issue for the analysis ofhistogram: There is no information regarding the numberof bins which need to quantize (18). For the betterment ofresults use the neural network for supervised and
unsupervised learning[17]. Basically neural network is theinterconnection between neurons. Artificial neural networkconsist of the number of artificial neurons. With the help ofneural network, supervised and unsupervised learningtechniques display the good results. Multilayer perceptionuses recurrent networks and feed forward neuralnetwork(as shown in Figure 2) [18]. It is the propertywhich consist of non linear functions and having inputoutput patterns which involves the multiple inputs andoutputs. Figure 3 shows the Simple neural network.
Figure 2: Multilayer Perceptron
Figure 3: Simple Neural Network
IV.RELEVANCE FEEDBACK
The term relevance feedback was initiated into Contentbased image retrieval from the concept of textual basedinformation retrieval in 1998. Further this has become awell liked technique in CBIR. Relevance feedback is amanaged active technique which is used to ameliorate thesuccess of information system. The fundamental scheme isto use the positive and negative instances from the user toenhance the system performance [2]. If the user accepts theimages as(positive examples) applicable to the query or(negative examples) not applicable. Then the user gives theresponse in the form of “Relevance feedback” indicatesover the extracted outcomes. Until the user is not satisfied,the process can continue. Relevance feedback strategyreally helps to enhance the semantic gap problem.
V.APPLICATIONS
Content based image retrieval has been used in variousfields for different purposes. Some applications are asfollow:Medical: The benefits of CBIR can consequence in thevarious services that can use in biomedical informationsystems. Large number of domains takes the advantage ofCBIR system[4]. Clinicians basically use similar cases forclinical decision-making process.Digital Libraries: The libraries support those serviceswhich are based on CBIR system.CrimeCulturalMilitaryEntertainment
Given table depicts the survey on various techniques anddataset on which SVM classifier is used.
TABLE I : REVIEW OF SOME RESEARCH PUBLICATIONS
VI. CONCLUSIONIn this paper, fundamentals for content based imageretrieval is introduced which include the visual contents,feature extraction, similarity/distance measures and userinteraction. The way the user communicates with thecontent based image retrieval system, the size of thedatabases, the features used and the speed of the retrievalare the most important factors that judge the success of aCBIR system. Moreover, it also reveals that the howalgorithms are used when SVM classifier is used for theextraction of various features of images to obtain thedesired results as per the user’s requirement.
REFERENCESInternational Journal of Computer Science and Mobile Computing,International Journal of Computer Science and Mobile Computing,, pg.769-775, 2014.Dr. Fuhui Long, Dr. Hongjiang Zhang and Prof. David Dagan FengFundamentals Of Content-Based Image Retrieval.da Silva Torres, Ricardo and Falc,Content-Based Image Retrieval: Theoryand Applications, Institute of Computing, State University of Campinas,Campinas, SP, Brazil,vol.13,no.2,pp.161-185,2006.Fundamental of Content Based Image Retrieval, International Journal ofComputer Science and Information Technologies,no.3260 – 3263,2012.M.E. El Alami,A new matching strategy for content based image retrievalsystem , , Applied Soft Computing,vol.14,pp.407-418,2014
unsupervised learning[17]. Basically neural network is theinterconnection between neurons. Artificial neural networkconsist of the number of artificial neurons. With the help ofneural network, supervised and unsupervised learningtechniques display the good results. Multilayer perceptionuses recurrent networks and feed forward neuralnetwork(as shown in Figure 2) [18]. It is the propertywhich consist of non linear functions and having inputoutput patterns which involves the multiple inputs andoutputs. Figure 3 shows the Simple neural network.
Figure 2: Multilayer Perceptron
Figure 3: Simple Neural Network
IV.RELEVANCE FEEDBACK
The term relevance feedback was initiated into Contentbased image retrieval from the concept of textual basedinformation retrieval in 1998. Further this has become awell liked technique in CBIR. Relevance feedback is amanaged active technique which is used to ameliorate thesuccess of information system. The fundamental scheme isto use the positive and negative instances from the user toenhance the system performance [2]. If the user accepts theimages as(positive examples) applicable to the query or(negative examples) not applicable. Then the user gives theresponse in the form of “Relevance feedback” indicatesover the extracted outcomes. Until the user is not satisfied,the process can continue. Relevance feedback strategyreally helps to enhance the semantic gap problem.
V.APPLICATIONS
Content based image retrieval has been used in variousfields for different purposes. Some applications are asfollow:Medical: The benefits of CBIR can consequence in thevarious services that can use in biomedical informationsystems. Large number of domains takes the advantage ofCBIR system[4]. Clinicians basically use similar cases forclinical decision-making process.Digital Libraries: The libraries support those serviceswhich are based on CBIR system.CrimeCulturalMilitaryEntertainment
Given table depicts the survey on various techniques anddataset on which SVM classifier is used.
TABLE I : REVIEW OF SOME RESEARCH PUBLICATIONS
VI. CONCLUSIONIn this paper, fundamentals for content based imageretrieval is introduced which include the visual contents,feature extraction, similarity/distance measures and userinteraction. The way the user communicates with thecontent based image retrieval system, the size of thedatabases, the features used and the speed of the retrievalare the most important factors that judge the success of aCBIR system. Moreover, it also reveals that the howalgorithms are used when SVM classifier is used for theextraction of various features of images to obtain thedesired results as per the user’s requirement.
REFERENCESInternational Journal of Computer Science and Mobile Computing,International Journal of Computer Science and Mobile Computing,, pg.769-775, 2014.Dr. Fuhui Long, Dr. Hongjiang Zhang and Prof. David Dagan FengFundamentals Of Content-Based Image Retrieval.da Silva Torres, Ricardo and Falc,Content-Based Image Retrieval: Theoryand Applications, Institute of Computing, State University of Campinas,Campinas, SP, Brazil,vol.13,no.2,pp.161-185,2006.Fundamental of Content Based Image Retrieval, International Journal ofComputer Science and Information Technologies,no.3260 – 3263,2012.M.E. El Alami,A new matching strategy for content based image retrievalsystem , , Applied Soft Computing,vol.14,pp.407-418,2014
unsupervised learning[17]. Basically neural network is theinterconnection between neurons. Artificial neural networkconsist of the number of artificial neurons. With the help ofneural network, supervised and unsupervised learningtechniques display the good results. Multilayer perceptionuses recurrent networks and feed forward neuralnetwork(as shown in Figure 2) [18]. It is the propertywhich consist of non linear functions and having inputoutput patterns which involves the multiple inputs andoutputs. Figure 3 shows the Simple neural network.
Figure 2: Multilayer Perceptron
Figure 3: Simple Neural Network
IV.RELEVANCE FEEDBACK
The term relevance feedback was initiated into Contentbased image retrieval from the concept of textual basedinformation retrieval in 1998. Further this has become awell liked technique in CBIR. Relevance feedback is amanaged active technique which is used to ameliorate thesuccess of information system. The fundamental scheme isto use the positive and negative instances from the user toenhance the system performance [2]. If the user accepts theimages as(positive examples) applicable to the query or(negative examples) not applicable. Then the user gives theresponse in the form of “Relevance feedback” indicatesover the extracted outcomes. Until the user is not satisfied,the process can continue. Relevance feedback strategyreally helps to enhance the semantic gap problem.
V.APPLICATIONS
Content based image retrieval has been used in variousfields for different purposes. Some applications are asfollow:Medical: The benefits of CBIR can consequence in thevarious services that can use in biomedical informationsystems. Large number of domains takes the advantage ofCBIR system[4]. Clinicians basically use similar cases forclinical decision-making process.Digital Libraries: The libraries support those serviceswhich are based on CBIR system.CrimeCulturalMilitaryEntertainment
Given table depicts the survey on various techniques anddataset on which SVM classifier is used.
TABLE I : REVIEW OF SOME RESEARCH PUBLICATIONS
VI. CONCLUSIONIn this paper, fundamentals for content based imageretrieval is introduced which include the visual contents,feature extraction, similarity/distance measures and userinteraction. The way the user communicates with thecontent based image retrieval system, the size of thedatabases, the features used and the speed of the retrievalare the most important factors that judge the success of aCBIR system. Moreover, it also reveals that the howalgorithms are used when SVM classifier is used for theextraction of various features of images to obtain thedesired results as per the user’s requirement.
REFERENCESInternational Journal of Computer Science and Mobile Computing,International Journal of Computer Science and Mobile Computing,, pg.769-775, 2014.Dr. Fuhui Long, Dr. Hongjiang Zhang and Prof. David Dagan FengFundamentals Of Content-Based Image Retrieval.da Silva Torres, Ricardo and Falc,Content-Based Image Retrieval: Theoryand Applications, Institute of Computing, State University of Campinas,Campinas, SP, Brazil,vol.13,no.2,pp.161-185,2006.Fundamental of Content Based Image Retrieval, International Journal ofComputer Science and Information Technologies,no.3260 – 3263,2012.M.E. El Alami,A new matching strategy for content based image retrievalsystem , , Applied Soft Computing,vol.14,pp.407-418,2014
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