Learning in content-based image retrieval

T. S. Huang, Xiang Sean Zhou, M. Nakazato, Ying Wu, I. Cohen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper we address several aspects of the learning problem in content-based image retrieval (CBIR). First, we introduce the linear and kernel-based biased discriminant analysis, or BiasMap, to fit the unique nature of relevance feedback as a small sample biased classification problem. Secondly, a WARF (word association via relevance feedback) formula is presented for learning keyword relations during the process of relevance feedback. We also introduce our new user interface for CBIR, ImageGrouper, which is designed to support more sophisticated user feedbacks and annotations. Finally, we use the D-EM (Discriminant-EM) algorithm as a way of exploiting unlabeled data in CBIR and offer some insights as to when unlabeled data will help.

Original languageEnglish (US)
Title of host publicationProceedings - 2nd International Conference on Development and Learning, ICDL 2002
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages155-162
Number of pages8
ISBN (Electronic)0769514596, 9780769514598
DOIs
StatePublished - 2002
Event2nd International Conference on Development and Learning, ICDL 2002 - Cambridge, United States
Duration: Jun 12 2002Jun 15 2002

Publication series

NameProceedings - 2nd International Conference on Development and Learning, ICDL 2002

Other

Other2nd International Conference on Development and Learning, ICDL 2002
Country/TerritoryUnited States
CityCambridge
Period6/12/026/15/02

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications

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