Texture Image Classification Using Support Vector Machine Mr.S.R.Suralkar Mr.A.H.Karode Ms.Priti W.Pawade Asso.Prof. (E & TC Dept.) Asst. Prof. (E & TC Dept.) M.E. 2 nd Year (Digital Electronics) SSBT’s COET, Bambhori, SSBT’s COET, Bambhori, SSBT’s COET, Bambhori, Jalgaon. Jalgaon. Jalgaon. [email protected][email protected]priti_pawade6 @rediffmail.com Abstract Texture refers to properties that represent the surface or structure of an object and is defined as something consisting of mutually related elements. The main focus in this study is to do texture segmentation and classification for texture images. Statistical features can be calculated based on the grey level co-occurrence probabilities (GLCP) generated. The statistical features used in this study are uniformity, contrast, and entropy. The features are obtained by using a combination of different angles. For noise reduction, an appropriate moving average is applied to the statistical features. To post- process the image, support vector machines (SVM) had been proposed to do classification on the extracted features. Some kernel functions which are being tested are second degree polynomial, radial basis function (RBF), exponential radial basis function (ERBF), sigmoid, and odd-order Bspline. RBF and ERBF achieved the best classification accuracy compare to other kernels used. SVM also automatically helps RBF kernel to define the centres during optimization. Brodatz texture album is used in this study to test out the result. In the study, a combined GLCP with SVM post-processing showed a marked improvement over other classifier in terms of classification accuracy. Keywords: Support Vector Machines, Grey Level Co-occurrence Probabilities, Image segmentation, Texture Classification 1. INTRODUCTION Texture is defined as a pattern that is repeated and is represented on the surface or structure of an object. To separate textures into a single texture type, first we need to preserve spatial information for each texture. For instance, the manual grey level thresholding which does not provide the spatial information for each texture that could generate in appropriate segmentation result. Edge detection techniques used on texture image could result in noisy and discontinuous edges and therefore segmentation process becomes more complicated Grey level co-occurrence probabilities (GLCP) method is used as a texture descriptor in the process of feature extraction. The selection of certain texture is possible as it is based on the distribution in grey level co-occurrence matrix (GLCM). Boundaries that separate between textures can be created by searching the gradients in one-dimensional (1D) GLCP statistical features .The process of GLCP extraction is arbitrary and takes unreliable time. Some approaches had modified the structure of GLCP algorithm in order to speed up the computation time for texture feature extraction process. We present a novel texture classification algorithm using Grey Level Co-occurrence Probabilities (GLCP) method is being used to extract features from texture image and support vector machines (SVM)[4]. Grey Level Co- occurrence Probabilities (GLCP) statistics are used to preserve the spatial characteristics of a texture. The selection of certain texture is possible based on the statistical features[5]. The best statistical features that are used for analysis are entropy, contrast, homogeneity and correlation. However, further analysis in shows that correlation was not suitable for texture segmentation. GLCP statistics can also be used to discriminate between two different textures. This feature vector is first used for classification of the extracted features using the GSVM (Gaussian SVM) classifier. The experimental setup consists of images from the Brodatz texture databases and a combination of some images therein. The proposed method produces promising classification results for both single and multiple class texture analysis problems[ 4]. 2. SVM – An Introductory Overview In the context of supervised classification, machine learning and pattern recognition is the extraction of regularity or some sort of structure from a collection of data. Neural networks (NN) and Bayesian classifiers are the typical examples to learn such organization from the given data observations. Support Vector Machines (SVM) is a relatively new classifier and is based on strong foundations from the broad area of statistical Priti W Pawade et al,Int.J.Comp.Tech.Appl,Vol 3 (1), 71-75 IJCTA | JAN-FEB 2012 Available [email protected]71 ISSN:2229-6093
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Texture Image Classification Using Support Vector Machine
Mr.S.R.Suralkar Mr.A.H.Karode Ms.Priti W.Pawade
Asso.Prof. (E & TC Dept.) Asst. Prof. (E & TC Dept.) M.E. 2nd Year (Digital Electronics) SSBT’s COET, Bambhori, SSBT’s COET, Bambhori, SSBT’s COET, Bambhori,