Advances and Applications in Statistics
Volume 3, Issue 3, Pages 243 - 254
(December 2003)
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NEURAL NETWORK-BASED IMAGE RETRIEVAL USING NONLINEAR COMBINING OF HETEROGENEOUS FEATURES
Teag-Hee Lee (Korea), Bo-Hyun Yun (Korea) and Young-Chul Kim (Korea)
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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
BackPropagation-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. |
Keywords and phrases: content-based image retrieval, neural network-based image retrieval, nonlinear combining. |
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