A color clustering technique for image segmentation
Computer Vision, Graphics, and Image Processing
Color quantization by dynamic programming and principal analysis
ACM Transactions on Graphics (TOG)
A comparison of clustering algorithms applied to color image quantization
Pattern Recognition Letters - special issue on pattern recognition in practice V
Gray-level reduction using local spatial features
Computer Vision and Image Understanding
Color image quantization for frame buffer display
SIGGRAPH '82 Proceedings of the 9th annual conference on Computer graphics and interactive techniques
Color reduction using a multi-stage Kohonen Self-Organizing Map with redundant features
Expert Systems with Applications: An International Journal
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A new technique suitable for reduction of the number of colors in an image is presented in this paper. It is based on histogram processing and the use of Kohonen Self Organizing Feature Map (SOFM) neural networks. Initially, the dominant colors of each primary image are extracted through a simple linear piece-wise histogram approximation process. Then, using a SOFM the dominant color components of each primary color band are obtained and a look up table is constructed containing all possible color triplets. The final dominant colors are extracted from the look-up table entries using a SOFM by specifying the number of output neurons equal to the number of the dominant colors. Thus, the final image has all the dominant color classes. Experimental and comparative results demonstrate the applicability of the proposed technique.