NOISE TOLERANT COLOR IMAGE SEGMENTATION USING SUPPORT VECTOR MACHINE

R. Gnana Vargin, R.V.V. Krishna

Abstract: Image segmentation is an important tool in image processing and can serve as an efficient front end to sophisticated algorithms. In this paper we present a noise tolerant color image segmentation using pixel wise support vector machine classification(SVM).Firstly the pixel level color feature and texture feature of the image ,which is used as input of SVM model (classifier) are extracted through the local homogeneity model and gabor filter. Then the SVM model (classifier) is trained by using PFCM which works efficiently even for noisy images with the extracted pixel level features. Finally the color image is segmented with the trained SVM model (classifier).Experimental evidence shows that the proposed method has a very effective segmentation results and computational behavior. The clustering techniques for color image segmentation using FCM and PFCM are being analyzed. The objective of this paper is to compare the clustering techniques for color images using peak –signal to noise ratio (PSNR), Accuracy, convergence rate, specificity and sensitivity

Keywords: segmentation, FCM, PFCM, SVM classifier, PSNR, Accuracy, convergence rate, specificity and sensitivity.

DOI: https://doi.org/10.15623/ijret.2014.0316006