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Proceedings Paper

Entropy-constrained learning vector quantization algorithms and their application in image compression
Author(s): Nicolaos B. Karayiannis
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Paper Abstract

This paper presents entropy constrained fuzzy clustering and learning vector quantization algorithms and their application in image compression. Entropy constrained fuzzy clustering (ECFC) algorithms were developed by minimizing an objective function incorporating the fuzzy partition entropy and the average distortion between the feature vectors, which represent the image data, and the prototypes, which represent the codevectors or codewords. The reformulation of fuzzy c-means (FCM) algorithms provided the basis for the development of fuzzy learning vector quantization (FLVQ) algorithms and essentially established a link between clustering and learning vector quantization. Minimization of the reformulation function that corresponds to ECFC algorithms using gradient descent results in entropy constrained learning vector quantization (ECLVQ) algorithms. These algorithms allow the gradual transition from a maximally fuzzy partition to a nearly crisp partition of the feature vectors during the learning process. This paper presents two alternative implementations of the proposed algorithms, which differ in terms of the strategy employed for updating the prototypes during learning. The proposed algorithms are tested and evaluated on the design of codebooks used for image data compression.

Paper Details

Date Published: 1 April 1997
PDF: 12 pages
Proc. SPIE 3030, Applications of Artificial Neural Networks in Image Processing II, (1 April 1997); doi: 10.1117/12.269766
Show Author Affiliations
Nicolaos B. Karayiannis, Univ. of Houston (United States)


Published in SPIE Proceedings Vol. 3030:
Applications of Artificial Neural Networks in Image Processing II
Nasser M. Nasrabadi; Aggelos K. Katsaggelos, Editor(s)

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