Dynamic semantic feature-based long-term cross-session learning approach to content-based image retrieval

Zhongmiao Xiao, Matthew J. Clark, Kok Sheik Wong, Xiaojun Qi

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

3 Citations (Scopus)

Abstract

This paper proposes a novel content-based image retrieval technique, which facilitates short-term (intra-query) and long-term (inter-query) learning processes by integrating accumulated users' historical relevance feedback-based semantic knowledge. The history is efficiently represented as a dynamic semantic feature of the images. As such, the high-level semantic similarity measure can be dynamically adapted based on the semantic relevance derived from the dynamic semantic features. The short-term relevance feedback technique can benefit from long-term learning. Our extensive experiments show that the proposed system outperforms three peer systems in the context of both correct and erroneous relevance feedback.

Original languageEnglish
Title of host publication2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings
Pages1033-1036
Number of pages4
DOIs
Publication statusPublished - 2012
Externally publishedYes
EventIEEE International Conference on Acoustics, Speech and Signal Processing 2012 - Kyoto International Conference Center, Kyoto, Japan
Duration: 25 Mar 201230 Mar 2012
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6268628 (Conference Proceedings)

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

ConferenceIEEE International Conference on Acoustics, Speech and Signal Processing 2012
Abbreviated titleICASSP 2012
CountryJapan
CityKyoto
Period25/03/1230/03/12
Internet address

Keywords

  • CBIR
  • crosssession learning
  • dynamic semantic feature
  • inter-query learning
  • relevance feedback

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