Publication Type

Journal Article

Version

publishedVersion

Publication Date

5-2009

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

Keywords

Active learning, Batch mode active learning, Content-based image retrieval, Human-computer interaction, Semisupervised learning, Support vector machines

Discipline

Computer Sciences | Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

ACM Transactions on Informations Systems

Volume

27

Issue

3

First Page

16-1

Last Page

7

ISSN

1063-6919

Identifier

10.1145/1508850.1508854

Publisher

ACM

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1145/1508850.1508854

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