Color quantization by dynamic programming and principal analysis
ACM Transactions on Graphics (TOG)
Statistical analysis of self-organization
Neural Networks
A comparison of clustering algorithms applied to color image quantization
Pattern Recognition Letters - special issue on pattern recognition in practice V
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Introduction to the Theory of Neural Computation
Introduction to the Theory of Neural Computation
Engineering Applications of Artificial Intelligence
Sample-size adaptive self-organization map for color images quantization
Pattern Recognition Letters
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Review article: Local adaptive receptive field self-organizing map for image color segmentation
Image and Vision Computing
LISA: Image Compression Scheme Based on an Asymmetric Hierarchical Self-Organizing Map
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Novel fast color reduction algorithm for time-constrained applications
Journal of Visual Communication and Image Representation
Self-organizing map-based color palette for high-dynamic range texture compression
Neural Computing and Applications - Special Issue on ICONIP2010
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Dynamic self-organizing maps with controlled growth for knowledge discovery
IEEE Transactions on Neural Networks
The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data
IEEE Transactions on Neural Networks
New adaptive color quantization method based on self-organizing maps
IEEE Transactions on Neural Networks
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In this paper, a new hierarchical color quantization method based on self-organizing maps that provides different levels of quantization is presented. Color quantization (CQ) is a typical image processing task, which consists of selecting a small number of code vectors from a set of available colors to represent a high color resolution image with minimum perceptual distortion. Several techniques have been proposed for CQ based on splitting algorithms or cluster analysis. Artificial neural networks and, more concretely, self-organizing models have been usually utilized for this purpose. The self-organizing map (SOM) is one of the most useful algorithms for color image quantization. However, it has some difficulties related to its fixed network architecture and the lack of representation of hierarchical relationships among data. The growing hierarchical SOM (GHSOM) tries to face these problems derived from the SOM model. The architecture of the GHSOM is established during the unsupervised learning process according to the input data. Furthermore, the proposed color quantizer allows the evaluation of different color quantization rates under different codebook sizes, according to the number of levels of the generated neural hierarchy. The experimental results show the good performance of this approach compared to other quantizers based on self-organization.