Fast training of SVM via morphological clustering for color image segmentation

Authors:
Yi Fang;Chen Pan;Li Liu;Lei Fang
Affiliations:
Key Laboratory of Biomedical Information Engineering of Education Ministry, Xi'an Jiaotong University, Xi'an, China;Key Laboratory of Biomedical Information Engineering of Education Ministry, Xi'an Jiaotong University, Xi'an, China;Key Laboratory of Biomedical Information Engineering of Education Ministry, Xi'an Jiaotong University, Xi'an, China;Key Laboratory of Biomedical Information Engineering of Education Ministry, Xi'an Jiaotong University, Xi'an, China
Venue:
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Year:
2005

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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.