Fast training of SVM via morphological clustering for color image segmentation

Abstract

A novel method of designing efficient SVM for fast color image segmentation is proposed in this paper. For application of large-scale image data, a new approach to initializing training set via pre-selecting useful training samples is adopted. By using a morphological unsupervised clustering technique, samples at the boundary of each cluster are selected for SVM training. With the proposed method, various experiments are carried out on the color blood cell images. Results show that the training set and time can be decreased considerably without lose of any segmentation accuracy. © Springer-Verlag Berlin Heidelberg 2005.