Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Competitive learning algorithms for vector quantization
Neural Networks
Statistical analysis of self-organization
Neural Networks
Pairwise Data Clustering by Deterministic Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Systolic Memory Architecture for Fast Codebook Design based on MMPDCL Algorithm
ITCC '04 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2 - Volume 2
Asymptotically optimal block quantization
IEEE Transactions on Information Theory
Minimax partial distortion competitive learning for optimal codebook design
IEEE Transactions on Image Processing
New adaptive color quantization method based on self-organizing maps
IEEE Transactions on Neural Networks
Generalized clustering networks and Kohonen's self-organizing scheme
IEEE Transactions on Neural Networks
Review article: Local adaptive receptive field self-organizing map for image color segmentation
Image and Vision Computing
Exploiting a Growing Self-organizing Map for Adaptive and Efficient Color Quantization
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Color quantization using principal components for initialization of Kohonen Sofm
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Hierarchical Color Quantization Based on Self-organization
Journal of Mathematical Imaging and Vision
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The paper presents a sample-size adaptive SOM (SA-SOM) algorithm for color quantization of images to adapt to the variations of network parameters and training sample size. The sweep size of neighborhood function is modulated by the size of the training data. In addition, the minimax distortion principle which is modulated by training sample size is used to search winning neuron. Based on the SA-SOM, we use the sampling ratio of training data, rather than the conventional weight change between adjacent sweeps, as a stop criterion, to significantly speed up the learning process. The experimental results show that the SA-SOM achieves much better PSNR quality, and smaller PSNR variation under various combinations of network parameters.