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Color reduction using local features and a kohonen self-organized feature map neural network

✍ Scribed by Nikos Papamarkos

Publisher: John Wiley and Sons
Year: 1999
Tongue: English
Weight: 519 KB
Volume: 10
Category: Article
ISSN: 0899-9457
DOI: 10.1002/(sici)1098-1098(1999)10:5<404::aid-ima5>3.0.co;2-f

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✦ Synopsis

This paper proposes a new method for reducing the number of colors in an image. The proposed approach uses both the image color components and local image characteristics to feed a Kohonen self-organized feature map (SOFM) neural network. After training, the neurons of the output competition layer define the proper color classes. The final image has the dominant image colors and its texture approaches the image local characteristics used. To speed up the entire algorithm and reduce memory requirements, a fractal scanning subsampling technique can be used. The method is applicable to all types of color images and can be easily extended to accommodate any type of spatial characteristics. Several experimental and comparative results are presented.

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