Self-organizing maps
An adaptive clustering algorithm for color quantization
Pattern Recognition Letters
Gray-level reduction using local spatial features
Computer Vision and Image Understanding
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Color image quantization for frame buffer display
SIGGRAPH '82 Proceedings of the 9th annual conference on Computer graphics and interactive techniques
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This paper proposes a new algorithm for color quantization. The proposed approach achieves color quantization using an adaptive tree clustering procedure. In each node of the tree a self-organized Neural Network Classifier (NNC) is used which is fed by image color values and additional local spatial features. The NNC consists of a Principal Component Analyzer (PCA) and a Kohonen self-organized feature map (SOFM) neural network. The output neurons of the NNC define the color classes for each node. The final image not only has the dominant image colors, but also its texture approaches the image local characteristics used. For better classification, split and merging conditions are used in order to define if color classes must be split or merged. To speed-up the entire algorithm and reduce memory requirements, a fractal scanning subsampling technique is used.