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.
|Number of pages||10|
|Journal||Proceedings of SPIE - The International Society for Optical Engineering|
|State||Published - 1999|
|Event||Proceedings of the 1999 Applications and Sciences of Neural Networks, Fuzzy Systems, and Evolutionary Computation II - Denver, CO, USA|
Duration: Jul 19 1999 → Jul 20 1999