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Proceedings Paper

Generalized clustering using optimization from a statistical mechanics approach
Author(s): Sujit Joshi; Sunanda Mitra
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Paper Abstract

This work evolves from the concept of deterministic annealing (DA) as a useful tool to solve non-convex optimization problems. DA is used in order to avoid local minima of the given application specific cost function in which traditional techniques get trapped. It is derived within a probabilistic framework from basic information theoretic principles. The application specific cost is minimized subject to a level of randomness (Shannon entropy), which is gradually lowered. A hard (non random) solution emerges at the limit of low temperature after the system goes through an annealing process. This paper deals with the important and useful application of DA to vector quantization of images. An extension of the basic algorithm by incorporating a structural constraint of mass or density is used to allow optimization of vector quantizers. The constrained algorithm is modified to work for a set of systems to generate a more generalized codebook. Experimental results show considerable performance gains over conventional methods.

Paper Details

Date Published: 1 November 1999
PDF: 10 pages
Proc. SPIE 3812, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation II, (1 November 1999); doi: 10.1117/12.367684
Show Author Affiliations
Sujit Joshi, Texas Tech Univ. (United States)
Sunanda Mitra, Texas Tech Univ. (United States)

Published in SPIE Proceedings Vol. 3812:
Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation II
Bruno Bosacchi; David B. Fogel; James C. Bezdek, Editor(s)

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