Adaptive color reduction

Abstract--This paper proposes a new algorithm for the reduction of the number of colors in an image. The proposed adaptive color reduction (ACR) technique achieves color reduction using a 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 (NN). The output neurons of the NNC define the color classes for each node. The final image not only has the dominant image colors, but its texture also approaches the image local characteristics used. Using the adaptive procedure and different local features for each level of the tree, the initial color classes can be split even more. 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 ...

• Type: Journal
• Year: 2002
• Where: TSMC
• Authors: Nikos Papamarkos, Antonios Atsalakis, Charalambos Strouthopoulos
• Keywords: Color, Color Classes, Color Reduction, TSMC 2002