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Proceedings Paper

Relevance feedback techniques in interactive content-based image retrieval
Author(s): Yong Rui; Thomas S. Huang; Sharad Mehrotra
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Paper Abstract

Content-based image retrieval (CBIR) has become one of the most active research areas in the past few years. Many visual feature representations have been explored and many systems built. While these research efforts establish the basis of CBIR, the usefulness of the proposed approaches is limited. Specifically, these efforts have relatively ignored two distinct characteristics of CBIR systems: (1) the gap between high level concepts and low level features; (2) subjectivity of human perception of visual content. This paper proposes a relevance feedback based interactive retrieval approach, which effectively takes into account the above two characteristics in CBIR. During the retrieval process, the user's high level query and perception subjectivity are captured by dynamically updated weights based on the user's relevance feedback. The experimental results show that the proposed approach greatly reduces the user's effort of composing a query and captures the user's information need more precisely.

Paper Details

Date Published: 23 December 1997
PDF: 12 pages
Proc. SPIE 3312, Storage and Retrieval for Image and Video Databases VI, (23 December 1997); doi: 10.1117/12.298455
Show Author Affiliations
Yong Rui, Univ. of Illinois/Urbana-Champaign (United States)
Thomas S. Huang, Univ. of Illinois/Urbana-Champaign (United States)
Sharad Mehrotra, Univ. of Illinois/Urbana-Champaign (United States)

Published in SPIE Proceedings Vol. 3312:
Storage and Retrieval for Image and Video Databases VI
Ishwar K. Sethi; Ramesh C. Jain, Editor(s)

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