Abstract
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 language | English |
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Title of host publication | IEEE International Conference on Multi-Media and Expo |
Pages | 713-716 |
Number of pages | 4 |
Edition | II/TUESDAY |
Publication status | Published - 2000 Dec 1 |
Event | 2000 IEEE International Conference on Multimedia and Expo (ICME 2000) - New York, NY, United States Duration: 2000 Jul 30 → 2000 Aug 2 |
Other
Other | 2000 IEEE International Conference on Multimedia and Expo (ICME 2000) |
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Country/Territory | United States |
City | New York, NY |
Period | 00/7/30 → 00/8/2 |
ASJC Scopus subject areas
- Engineering(all)