A genetic approach towards optimal color image quantization

In this paper the problem of local optimality of color image quantization procedures is discussed. The well-known and frequently used C-means clustering algorithm (CMA) is applied to the problem, and its dependence on initial conditions is studied. A hybrid approach, combining CMA with a genetic alg...

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Bibliographic Details
Published in:1996 IEEE International Conference on Image Processing Proceedings Vol. 3; pp. 1031 - 1034 vol.3
Main Author: Scheunders, P.
Format: Conference Proceeding
Language:English
Published: IEEE 1996
Subjects:
Clustering algorithms
Color
Displays
Genetic algorithms
Genetic mutations
Humans
Machine vision
Physics
Pixel
Quantization
ISBN:9780780332591, 0780332598
Online Access:Get full text
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Summary:In this paper the problem of local optimality of color image quantization procedures is discussed. The well-known and frequently used C-means clustering algorithm (CMA) is applied to the problem, and its dependence on initial conditions is studied. A hybrid approach, combining CMA with a genetic algorithm is constructed, and it is shown that this approach is insensitive to its initial conditions. Results compare the performance of the genetic approach with CMA on three different types of initial conditions: random initial conditions and two popular color image quantization algorithms: the median-cut algorithm and the variance-based algorithm. In all cases the genetic approach outperforms CMA.
ISBN:9780780332591
0780332598
DOI:10.1109/ICIP.1996.561008