Title :
Adaptive color reduction
Author :
Papamarkos, Nikos ; Atsalakis, Antonis E. ; Strouthopoulos, Charalampos P.
Author_Institution :
Dept. of Electr. & Comput. Eng., Democritus Univ. of Thrace, Xanthi, Greece
fDate :
2/1/2002 12:00:00 AM
Abstract :
The paper proposes an algorithm for reducing 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 whether color classes must be split or merged. To speed up the entire algorithm and reduce memory requirements, a fractal scanning subsampling technique is used. The method is independent of the color scheme, it is applicable to any type of color images, and it can be easily modified to accommodate any type of spatial features and any type of tree structure. Several experimental and comparative results, exhibiting the performance of the proposed technique, are presented
Keywords :
adaptive systems; fractals; image classification; image colour analysis; principal component analysis; self-organising feature maps; tree data structures; ACR; Kohonen self-organized feature map; NNC; PCA; SOFM neural network; adaptive color reduction; adaptive procedure; color classes; color quantization; color segmentation; fractal scanning subsampling technique; image color reduction; image color values; image local characteristics; initial color classes; local features; local spatial features; memory requirements; merging conditions; output neurons; principal component analyzer; self-organized neural network classifier; split conditions; tree clustering procedure; Circuit analysis computing; Clustering algorithms; Digital images; Image color analysis; Image segmentation; Laboratories; Neural networks; Principal component analysis; Quantization; Space technology;
Journal_Title :
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
DOI :
10.1109/3477.979959