Optimization of training samples with affinity propagation algorithm for multi-class SVM classification

Abstract

This paper presents a novel optimization method of training samples with Affinity Propagation (AP) clustering algorithm for multi-class Support Vector Machine (SVM) classification problem. The method of optimizing training samples is based on region clustering with affinity propagation algorithm. Then the multi-class support vector machines are trained for natural image classification with AP optimized samples. The feature space constructed in this paper is a composition of combined histogram with color, texture and edge descriptor of images. Experimental results show that better classification accuracy can be obtained by using the proposed method. © 2010 Springer-Verlag.