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| 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 |
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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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Received: 27 December 2010
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2012, Vol. 25 
