Generalized clustering using optimization from a statistical mechanics approach

Sujit Joshi; Sunanda Mitra

doi:10.1117/12.367684

1 November 1999 Generalized clustering using optimization from a statistical mechanics approach

Sujit Joshi, Sunanda Mitra

Proceedings Volume 3812, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation II; (1999) https://doi.org/10.1117/12.367684
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1999, Denver, CO, United States

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.

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Sujit Joshi and Sunanda Mitra "Generalized clustering using optimization from a statistical mechanics approach", Proc. SPIE 3812, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation II, (1 November 1999); https://doi.org/10.1117/12.367684

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KEYWORDS

Annealing

Distortion

Image compression

Optimization (mathematics)

Quantization

Image segmentation

Image enhancement

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