Publication Type

Journal Article

Version

publishedVersion

Publication Date

4-2006

Abstract

Relevance feedback has emerged as a powerful tool to boost the retrieval performance in content-based image retrieval (CBIR). In the past, most research efforts in this field have focused on designing effective algorithms for traditional relevance feedback. Given that a CBIR system can collect and store users' relevance feedback information in a history log, an image retrieval system should be able to take advantage of the log data of users' feedback to enhance its retrieval performance. In this paper, we propose a unified framework for log-based relevance feedback that integrates the log of feedback data into the traditional relevance feedback schemes to learn effectively the correlation between low-level image features and high-level concepts. Given the error-prone nature of log data, we present a novel learning technique, named Soft Label Support Vector Machine, to tackle the noisy data problem. Extensive experiments are designed and conducted to evaluate the proposed algorithms based on the COREL image data set. The promising experimental results validate the effectiveness of our log-based relevance feedback scheme empirically.

Keywords

Content-based image retrieval, log data, log-based relevance feedback, relevance feedback, semantic gap, support vector machines., user issues

Discipline

Databases and Information Systems | Theory and Algorithms

Research Areas

Data Science and Engineering

Publication

IEEE Transactions on Knowledge and Data Engineering (TKDE)

Volume

18

Issue

4

First Page

509

Last Page

524

ISSN

1041-4347

Identifier

10.1109/TKDE.2006.1599389

Publisher

IEEE

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TKDE.2006.1599389

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