Speed up image annotation based on LVQ technique with affinity propagation algorithm

Authors:
Song Lin;Yao Yao;Ping Guo
Affiliations:
School of Com. Sci. & Tech., Beijing Institute of Technology, Beijing, China and School of Com. & Info., Fujian Agriculture and Forestry University, Fujian, China;School of Com. Sci. & Tech., Beijing Institute of Technology, Beijing, China;School of Com. Sci. & Tech., Beijing Institute of Technology, Beijing, China and Lab. of Image Proc. & Patt. Recog., Beijing Normal University, Beijing, China
Venue:
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Year:
2010

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Abstract

For a support vector machine (SVM) classifier applied to image annotation, if too many training samples are used, the training speed might be very slow and also bring the problem of declining the classification accuracy. Learning vector quantization (LVQ) technique provides a framework to select some representative vectors which can be used to train the classifier instead of using original training data. A novel method which combines affinity propagation algorithm based LVQ technique and SVM classifier is proposed to annotate images. Experimental results demonstrate that proposed method has a better speed performance than that of SVM without applying LVQ.