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Proceedings Paper

Nonlinear combining of heterogeneous features in content-based image retrieval
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Paper Abstract

In content-based image retrieval (CBIR), retrieval based on different features can be various by the way how to combine the feature values. Most of the existing approaches assume a linear relationship between different features, and the usefulness of such systems was limited due to the difficulty in representing high-level concepts using low-level features. In this paper, we introduce Neural Network-based Image retrieval (NNIR) system, a human-computer interaction approach to CBIR. By using the Radial Basis Function (RBF) network, this approach determines nonlinear relationship between features so that more accurate similarity comparison between images can be supported. The experimental results show that the proposed approach has the superior retrieval performance than the existing linear combining approach, the rank-based method and the Back Propagatoin-based method. Although the proposed retrieval model is for CBIR, it can easily be expanded to handle other media types such as video and audio.

Paper Details

Date Published: 11 October 2000
PDF: 9 pages
Proc. SPIE 4197, Intelligent Robots and Computer Vision XIX: Algorithms, Techniques, and Active Vision, (11 October 2000); doi: 10.1117/12.403774
Show Author Affiliations
HyoungGu K. Lee, Seoul National Univ. (South Korea)
Suk In Yoo, Seoul National Univ. (South Korea)


Published in SPIE Proceedings Vol. 4197:
Intelligent Robots and Computer Vision XIX: Algorithms, Techniques, and Active Vision
David P. Casasent, Editor(s)

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