Semi-supervised learning for image retrieval using support vector machines

Authors:
Ke Lu;Jidong Zhao;Mengqin Xia;Jiazhi Zeng
Affiliations:
School of Computer Science and Engineering, University of Electronic Science & Technology of China, Chengdu, Sichuan, China;School of Computer Science and Engineering, University of Electronic Science & Technology of China, Chengdu, Sichuan, China;School of Computer Science and Engineering, University of Electronic Science & Technology of China, Chengdu, Sichuan, China;School of Computer Science and Engineering, University of Electronic Science & Technology of China, Chengdu, Sichuan, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Year:
2005

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Abstract

We study the problem of image retrieval based on semi-supervised learning. Semi-supervised learning has attracted a lot of attention in recent years. Different from traditional supervised learning. Semi-supervised learning makes use of both labeled and unlabeled data. In image retrieval, collecting labeled examples costs human efforts, while vast amounts of unlabelled data are often readily available and offer some additional information. In this paper, based on Support Vector Machine (SVM), we introduce a semi-supervised learning method for image retrieval. The basic consideration of the method is that, if two data points are close to each, they should share the same label. Therefore, it is reasonable to search a projection with maximal margin and locality preserving property. We compare our method to standard SVM and transductive SVM. Experimental results show efficiency and effectiveness of our method.