Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Introduction to the theory of neural computation
Introduction to the theory of neural computation
Vector quantization and signal compression
Vector quantization and signal compression
Statistical analysis of self-organization
Neural Networks
A Robust Competitive Clustering Algorithm With Applications in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
GGobi: evolving from XGobi into an extensible framework for interactive data visualization
Computational Statistics & Data Analysis - Data visualization
A new cluster validity measure and its application to image compression
Pattern Analysis & Applications
Alternative learning vector quantization
Pattern Recognition
Sample-size adaptive self-organization map for color images quantization
Pattern Recognition Letters
Asymptotically optimal block quantization
IEEE Transactions on Information Theory
Automatic gradient threshold determination for edge detection
IEEE Transactions on Image Processing
Weighted centroid neural network for edge preserving image compression
IEEE Transactions on Neural Networks
New adaptive color quantization method based on self-organizing maps
IEEE Transactions on Neural Networks
Adaptive learning method in self-organizing map for edge preserving vector quantization
IEEE Transactions on Neural Networks
Fast requantization using self organizing feature map with orthogonal polynomials transform
Proceedings of the 2011 International Conference on Communication, Computing & Security
An edge preserving requantization model for color image coding with orthogonal polynomials
Digital Signal Processing
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This paper presents a novel classified self-organizing map method for edge preserving quantization of images using an adaptive subcodebook and weighted learning rate. The subcodebook sizes of two classes are automatically adjusted in training iterations based on modified partial distortions that can be estimated incrementally. The proposed weighted learning rate updates the neuron efficiently no matter of how large the weighting factor is. Experimental results show that the new method achieves better quality of reconstructed edge blocks and more spread out codebook and incurs a significantly less computational cost as compared to the competing methods.