Color image quantization using interactive genetic algorithm
Pages 139 - 140
Abstract
Color image quantization is one of the most widely used image processing techniques, where the number of colors used in the image is to be reduced to a specific value.[2] The aim of the task is to minimize the distortion from the original image. In previous studies, this has been measured using the distance between the original color and the transformed color of corresponding pixels. This concept is straightforward and can easily be calculated, and thus has been used as a general measure. However, to find the color tuple which minimizes the difference is known to be NP-hard.[3] Therefore, a number of studies have been performed using several optimization techniques.
References
[1]
S. Bergen and B. J. Ross. Automatic and interactive evolution of vector graphics images with genetic algorithms. The Visual Computer, 28(1):35--45, 2012.
[2]
J.-P. Braquelaire and L. Brun. Comparison and optimization of methods of color image quantization. Image Processing, IEEE Transactions on, 6(7):1048--1052, 1997.
[3]
P. Brucker. On the complexity of clustering problems. In Optimization and operations research, pages 45--54. Springer, 1978.
[4]
B. Freisleben and A. Schrader. An evolutionary approach to color image quantization. In Evolutionary Computation, 1997., IEEE International Conference on, pages 459--464. IEEE, 1997.
[5]
M. G. Omran, A. P. Engelbrecht, and A. Salman. A color image quantization algorithm based on particle swarm optimization. informatica 29:261--269, 2005.
[6]
Q. Su and Z. Hu. Color image quantization algorithm based on self-adaptive differential evolution. Computational intelligence and neuroscience, 2013:3, 2013.
[7]
H. Takagi. Interactive evolutionary computation: Fusion of the capabilities of ec optimization and human evaluation. Proceedings of the IEEE, 89(9):1275--1296, 2001.
[8]
H. Takagi and N. Hayashida. Interactive ec-based signal processing. In 4th Asia-Pacific Conference on Simulated Evolution and Learning (SEAL2002), Singapore, pages 375--379, 2002.
[9]
A. G. Weber. The USC-SIPI Image Database. http://sipi.usc.edu/database/, Oct. 1997.
Index Terms
- Color image quantization using interactive genetic algorithm
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Published In
April 2015
2418 pages
ISBN:9781450331968
DOI:10.1145/2695664
- Conference Chairs:
- Roger L. Wainwright,
- Juan Manuel Corchado,
- Program Chairs:
- Alessio Bechini,
- Jiman Hong
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Published: 13 April 2015
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