Incremental Image Classification Method Based on Semi-Supervised Learning
LIANG Peng1,2, LI Shao-Fa2, QIN Jiang-Wei2, LUO Jian-Gao3
1.School of Computer Science and Engineering,Guangdong Polytechnic Normal University,Guangzhou 510665 2.School of Computer Science and Engineering,South China University of Technology,Guangzhou 510006 3. Department of Computer,Guangdong AIB Polytechnic College,Guangzhou 510507
Abstract In order to use large numbers of unlabeled images effectively, an image classification method is proposed based on semi-supervised learning. The proposed method bridges a large amount of unlabeled images and limited numbers of labeled images by exploiting the common topics. The classification accuracy is improved by using the must-link constraint and cannot-link constraint of labeled images. The experimental results on Caltech-101 and 7-classes image dataset demonstrate that the classification accuracy improves about 10% by the proposed method. Furthermore, due to the present semi-supervised image classification methods lacking of incremental learning ability, an incremental implementation of our method is proposed. Comparing with non-incremental learning model in literature, the incremental learning method improves the computation efficiency of nearly 90%.
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2012, Vol. 25 
