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http://dx.doi.org/10.5302/J.ICROS.2004.10.7.597

Image Clustering using Improved Neural Network Algorithm  

박상성 (고려대학교 산업시스템정보공학과)
이만희 (삼성테크)
유헌우 (연세대학교 인지과학연구)
문호석 (고려대학교 산업시스템정보공학)
장동식 (고려대학교 산업시스템정보공학과)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.10, no.7, 2004 , pp. 597-603 More about this Journal
Abstract
In retrieving large database of image data, the clustering is essential for fast retrieval. However, it is difficult to cluster a number of image data adequately. Moreover, current retrieval methods using similarities are uncertain of retrieval accuracy and take much retrieving time. In this paper, a suggested image retrieval system combines Fuzzy ART neural network algorithm to reinforce defects and to support them efficiently. This image retrieval system takes color and texture as specific feature required in retrieval system and normalizes each of them. We adapt Fuzzy ART algorithm as neural network which receive normalized input-vector and propose improved Fuzzy ART algorithm. The result of implementation with 200 image data shows approximately retrieval ratio of 83%.
Keywords
image retrieval; content-based; color; texture; features; neural network; ART; Fuzzy ART;
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