A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
International Journal of Computer Vision
VisualSEEk: a fully automated content-based image query system
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
The TV-tree: an index structure for high-dimensional data
The VLDB Journal — The International Journal on Very Large Data Bases - Spatial Database Systems
Similarity Indexing with the SS-tree
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
The X-tree: An Index Structure for High-Dimensional Data
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
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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.