NDLI: Semi-supervised SVM batch mode active learning for image retrieval

Content Provider	IEEE Xplore Digital Library
Author	Hoi, S.C.H. Rong Jin Jianke Zhu Lyu, M.R.
Copyright Year	2008
Description	Author affiliation: Sch. of Comput. Eng., Nanyang Technol. Univ., Singapore (Hoi, S.C.H.)
Abstract	Active learning has been shown as a key technique for improving content-based image retrieval (CBIR) performance. Among various methods, support vector machine (SVM) active learning is popular for its application to relevance feedback in CBIR. However, the regular SVM active learning has two main drawbacks when used for relevance feedback. First, SVM often suffers from learning with a small number of labeled examples, which is the case in relevance feedback. Second, SVM active learning usually does not take into account the redundancy among examples, and therefore could select multiple examples in relevance feedback that are similar (or even identical) to each other. In this paper, we propose a novel scheme that exploits both semi-supervised kernel learning and batch mode active learning for relevance feedback in CBIR. In particular, a kernel function is first learned from a mixture of labeled and unlabeled examples. The kernel will then be used to effectively identify the informative and diverse examples for active learning via a min-max framework. An empirical study with relevance feedback of CBIR showed that the proposed scheme is significantly more effective than other state-of-the-art approaches.
Starting Page	1
Ending Page	7
File Size	386496
Page Count	7
File Format	PDF
ISBN	9781424422425
ISSN	10636919
DOI	10.1109/CVPR.2008.4587350
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Image retrieval Machine learning Support vector machine classification Content based retrieval Kernel Application software State feedback Learning systems Sampling methods
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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