Clustering for image retrieval via improved Fuzzy-ART

Sang Sung Park, Hun Woo Yoo, Man Hee Lee, Jae Yeon Kim, Dong Sik Jang

Research output: Contribution to journalConference articlepeer-review

3 Citations (Scopus)


Clustering technique is essential for fast retrieval in large database. In this paper, new image clustering technique 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. Our new 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 our algorithm, experiment results on image clustering performance and comparison with original Fuzzy-ART are presented in terms of recall rates.

Original languageEnglish
Pages (from-to)743-752
Number of pages10
JournalLecture Notes in Computer Science
Issue numberIV
Publication statusPublished - 2005
EventInternational Conference on Computational Science and Its Applications - ICCSA 2005 - , Singapore
Duration: 2005 May 92005 May 12

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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