Relevance graph-based image retrieval

Sanghoon Sull, J. Oh, S. Oh, S. M H Song, S. W. Lee

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

2 Citations (Scopus)


The basic limitation of content-based image retrieval and relevance feedback based on low-level image features is that low-level features are often highly ineffective for representing not only content similarity but conceptual and contextual similarity between images. On the other hand, the utility of text-based image retrieval is restricted due to the limited availability of image annotations and textual description's limited ability in describing image content. In this paper, we introduce a novel approach to content-, concept- and context-based image retrieval that utilizes user-established relevance between images only using image links without relying on image features or textual annotations. We present a framework for accumulating image relevance information through relevance feedback, determining the degree of relevance, and constructing a relevance graph for image database. The use of graph-theoretical algorithms is suggested for image search and experimental studies are presented to demonstrate the potential of the proposed methods.

Original languageEnglish
Title of host publicationIEEE International Conference on Multi-Media and Expo
Number of pages4
Publication statusPublished - 2000 Dec 1
Event2000 IEEE International Conference on Multimedia and Expo (ICME 2000) - New York, NY, United States
Duration: 2000 Jul 302000 Aug 2


Other2000 IEEE International Conference on Multimedia and Expo (ICME 2000)
Country/TerritoryUnited States
CityNew York, NY

ASJC Scopus subject areas

  • Engineering(all)


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