An evolution-based tabu search approach to codebook design
Pattern Recognition
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Artificial Intelligence in Medicine
Fundamenta Informaticae
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The authors present a unified formulation and study of vector quantizer design methods that couple stochastic relaxation (SR) techniques with the generalized Lloyd algorithm. Two new SR techniques are investigated and compared: simulated annealing (SA) and a reduced-complexity approach that modifies the traditional acceptance criterion for simulated annealing to an unconditional acceptance of perturbations. It is shown that four existing techniques all fit into a general methodology for vector quantizer design aimed at finding a globally optimal solution. Comparisons of the algorithms' performances when quantizing Gauss-Markov processes, speech, and image sources are given. The SA method is guaranteed to perform in a globally optimal manner, and the SR technique gives empirical results equivalent to those of SA. Both techniques result in significantly better performance than that obtained with the generalized Lloyd algorithm