Expert system based on artificial neural networks for content-based image retrieval
- Authors
- Park, SS; Seo, KK; Jang, DS
- Issue Date
- 10월-2005
- Publisher
- PERGAMON-ELSEVIER SCIENCE LTD
- Keywords
- image clustering; content-based image retrieval; HSV joint histogram; gray-level co-occurrence matrix; fuzzy-ART
- Citation
- EXPERT SYSTEMS WITH APPLICATIONS, v.29, no.3, pp.589 - 597
- Indexed
- SCIE
SCOPUS
- Journal Title
- EXPERT SYSTEMS WITH APPLICATIONS
- Volume
- 29
- Number
- 3
- Start Page
- 589
- End Page
- 597
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/123215
- DOI
- 10.1016/j.eswa.2005.04.027
- ISSN
- 0957-4174
- Abstract
- Clustering technique is essential for fast retrieval in large database. In this paper, new image clustering technique based on artificial neural networks is proposed for content-based image retrieval. Fuzzy-ART mechanism maps high-dimensional input features into the output neuron. Joint HSV histogram and average entropy computed from gray-level co-occurrence matrices in the localized image region is employed as input feature elements. Original Fuzzy-ART suffers unnecessary increase of the number of output neurons when the noise input is presented. Modified Fuzzy-ART mechanism resolves the problem by differently updating the committed node and uncommitted node, and checking the vigilance test again. To show the validity of the proposed algorithm, experiment results on image clustering performance and comparison with original Fuzzy-ART are presented in terms of recall rates. (c) 2005 Elsevier Ltd. All rights reserved.
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Collections - College of Engineering > School of Industrial and Management Engineering > 1. Journal Articles
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