A Review of Feature Extraction Techniques for CBIR based on SVM

Int. Journal of Electrical & Electronics Engg.

Vol. 2, Spl. Issue 1 (2015)

e-ISSN: 1694-2310 | p-ISSN: 1694-2426

A Review of Feature Extraction Techniques for CBIR based on SVM Navneet Kaur1, Sonika Jindal2 1

M.Tech, Department of Computer Science and Engineering Assistant Professor, Department of Computer Science and Engineering Shaheed Bhagat Singh College of Engineering and Technology, Ferozepur 2

dhahnavneet@gmail.com & sonikamanoj@gmail.com

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.

Relevance Feedback Query Formation

Descrip tion Image Database

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Visual Content

Feature Vectors Feature Database

Similarity Comparison Indexing & Retrieval

Descript ion

Index Terms— Content Based Image Retrieval , Support Vector Machine , Feature Extraction, Relevance Feedback.

I. INTRODUCTION1 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.

Visual Content

Output

Retrieval results

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 116

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Vol. 2, Spl. Issue 1 (2015)

includes three colours components red, green and blue also known as ‘additive primaries’[4]. In contrast, for the purpose of printing CMY color space s used whose color components are cyan, magenta and yellow also known as ‘Subtractive Primaries’. Light absorption is produced color in CMY space. Color features have various advantages: Robustness Effectiveness Implementation Simplicity Computational Simplicity Low Storage Requirements TEXTURE Texture is one more another significant property of the images. It deals with the visual patterns which have the quality of uniformity or settlement that do not consequence from only the presence of single intensity. For the purpose of both computer vision and pattern recognition, different texture representations have been explored.

Texture representation can be divided into various classes: Structural methods: These methods include the graphs and morphological operators and their rules. This reveals with the action of image primitives and presence of parallel objects. The structural method introduces to retrieve the structural information under the assumption of human visual perception[4]. The main target image quality can be further subdivided on the basis of original image that is distortion free and another is distorted image. If reference of image is known that is called to be full reference otherwise no reference or it can be blind quality approach is obtained.

Moreover, another method introduces the reference image is available partially which consist of set of extracted features for evaluating the quality of distorted image.

Statistical methods These methods consist of famous co-occurrence matrix, Fourier power spectra, Shift invariant principal component analysis (SPCA), Tamura feature, Multi-resolution filtering technique such as Gabor and wavelet transform, characterize the texture by statistical distribution of the image intensity.

SHAPE Shape representation can be subdivided into two categories Boundary based which includes the outer boundary of the shape only. This is completed by describing the region which uses only the external features, such as the pixels along with the object boundary.

Region Based is completely different from the boundary based. This can be used the whole shape region by explaining the internal characteristics such as the pixels present in the region.

III.SUPPORT VECTOR MACHINE

SVM is the state of art classification method which is introduced in 1992 by Bose, guy on and Vapnik.SVM is an best tool for regression and classification.[16] Support vector machine may be defined as this is the linear 117

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function of the high dimensionality feature space which consist of the postulate space. SVM is a most beneficial technique for the data classification. Sometimes unsatisfied results are obtained using the neural networks and even it is easy to use. The classification task includes the training and testing data which consist the same data instances.

Each sample in the training set consist the target values and its various attributes.[17] The major goal of SVM is to judge that the target value of various data instances in the testing set which are given only the attributes of the data.

Classification in the SVM is the instance of supervised learning. Now discussion of various algorithms on which SVM is used.

Improved SVM also known as the SVM clustering.

Clustering is an unsupervised learning technique which simply means the decomposition of objects into various clusters and subgroups on the basis of similarity.

SVM with Gabor magnitude Gabor filters are the combination of wavelets, where individual wavelet which captures the energy at specific direction and frequency. For the detection of different orientation and frequencies, the Gabor filter banks are designed[17]. A hybrid approach to CBIR is used, SVM is trained and then therefore the database of images is labeled using feedback from users which consider relevant and non-relevant. Standard deviation of Gabor can be calculated to obtain the Gabor feature vector[17]. Various steps 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 particular scale.

Calculate standard deviation and mean, which gives the Gabor feature vector.

Image Retrieval using SVM and SURF Surf (Speeded up robust Features) is a local feature detector; first introduced by Herbert Bay et al in 2006 which is magnificent by SIFT descriptor.

Basically SURF is the combination of 2D-Haar wavelet which makes logical use of intrinsic images[18]. Using the histogram of gradient orientation, construct the descriptor vector of length 64[21]. Only CBIR with Surf and SVM method does not provide the better results, so that is why, use the CBIR with Surf ,Artificial Neural Network and SVM gives the improved results. The combination of modified SURF, Similarity matching algorithms and image blending 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 and NN. Display the results and obtained the average accuracy SVM with Quadratic Distance Metric For the extraction of colour features Global Colour histogram is used. There was an issue for the analysis of histogram: There is no information regarding the number of bins which need to quantize (18). For the betterment of results use the neural network for supervised and NITTTR, Chandigarh

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unsupervised learning[17]. Basically neural network is the interconnection between neurons. Artificial neural network consist of the number of artificial neurons. With the help of neural network, supervised and unsupervised learning techniques display the good results. Multilayer perception uses recurrent networks and feed forward neural network(as shown in Figure 2) [18]. It is the property which consist of non linear functions and having input output patterns which involves the multiple inputs and outputs. Figure 3 shows the Simple neural network.

e-ISSN: 1694-2310 | p-ISSN: 1694-2426

IV.RELEVANCE FEEDBACK The term relevance feedback was initiated into Content based image retrieval from the concept of textual based information retrieval in 1998. Further this has become a well liked technique in CBIR. Relevance feedback is a managed active technique which is used to ameliorate the success of information system. The fundamental scheme is to use the positive and negative instances from the user to enhance the system performance [2]. If the user accepts the images as(positive examples) applicable to the query or (negative examples) not applicable. Then the user gives the response in the form of “Relevance feedback” indicates over the extracted outcomes. Until the user is not satisfied, the process can continue. Relevance feedback strategy really helps to enhance the semantic gap problem.

V.APPLICATIONS

Paper

Technique

Dataset (Accuracy)

Sultan Aljahdali (2012)

Gabor Filter

COIL Dataset (89.5%)

Sukhmanjeet Kaur (2015)

SURF(Speeded Up Robust Feature)

98%

M.E ElAlami

ANN(Feed Forward Neural Network)

Wang dataset (67.2%)

Semi Supervised Approach

Corel Dataset

Fast Fourier transform

85.5%

Bearing fault Diagnosis and Gear Fault

-

Quadratic Distance Metric Algorithm

Corel Image dataset & benchmark dataset

Canberraa Distance And Gabor Wavelet

Wang dataset

GUI-ZHI LI,YA-HUI LIU,CHANGSHENG-ZHOU(2013) Rajesh Singla and Haseena B.A(2014) Xiukuan Zhao (2011) K.Ashok Kumar & Y.V.Bhaskar Reddy(2012) S. Mangijao Singh & K. Hemachandran(2012)

Content based image retrieval has been used in various fields for different purposes. Some applications are as follow: Medical: The benefits of CBIR can consequence in the various services that can use in biomedical information systems. Large number of domains takes the advantage of CBIR system[4]. Clinicians basically use similar cases for clinical decision-making process.

Digital Libraries: The libraries support those services which are based on CBIR system.

Crime Cultural Military Entertainment Given table depicts the survey on various techniques and dataset on which SVM classifier is used.

TABLE I : REVIEW OF SOME RESEARCH PUBLICATIONS

Figure 2: Multilayer Perceptron

X1 1 X2 h(.)

REFERENCES

xn h(.)

1

Sigmoidal Function

Figure 3: Simple Neural Network NITTTR, Chandigarh

VI. CONCLUSION In this paper, fundamentals for content based image retrieval is introduced which include the visual contents, feature extraction, similarity/distance measures and user interaction. The way the user communicates with the content based image retrieval system, the size of the databases, the features used and the speed of the retrieval are the most important factors that judge the success of a CBIR system. Moreover, it also reveals that the how algorithms are used when SVM classifier is used for the extraction of various features of images to obtain the desired results as per the user’s requirement.

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