Sample-size adaptive self-organization map for color images quantization
Pattern Recognition Letters
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
IEEE Transactions on Neural Networks
Improving the performance of k-means for color quantization
Image and Vision Computing
International Journal of Hybrid Intelligent Systems - Rough and Fuzzy Methods for Data Mining
Lossy image compression using a GHSOM
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
A computational intelligence scheme for the prediction of the daily peak load
Applied Soft Computing
Joint time-frequency and kernel principal component based SOM for machine maintenance
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Engineering Applications of Artificial Intelligence
Self-organizing maps with a time-varying structure
ACM Computing Surveys (CSUR)
An efficient color quantization based on generic roughness measure
Pattern Recognition
Hierarchical Color Quantization Based on Self-organization
Journal of Mathematical Imaging and Vision
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Color quantization (CQ) is an image processing task popularly used to convert true color images to palletized images for limited color display devices. To minimize the contouring artifacts introduced by the reduction of colors, a new competitive learning (CL) based scheme called the frequency sensitive self-organizing maps (FS-SOMs) is proposed to optimize the color palette design for CQ. FS-SOM harmonically blends the neighborhood adaptation of the well-known self-organizing maps (SOMs) with the neuron dependent frequency sensitive learning model, the global butterfly permutation sequence for input randomization, and the reinitialization of dead neurons to harness effective utilization of neurons. The net effect is an improvement in adaptation, a well-ordered color palette, and the alleviation of underutilization problem, which is the main cause of visually perceivable artifacts of CQ. Extensive simulations have been performed to analyze and compare the learning behavior and performance of FS-SOM against other vector quantization (VQ) algorithms. The results show that the proposed FS-SOM outperforms classical CL, Linde, Buzo, and Gray (LBG), and SOM algorithms. More importantly, FS-SOM achieves its superiority in reconstruction quality and topological ordering with a much greater robustness against variations in network parameters than the current art SOM algorithm for CQ. A most significant bit (MSB) biased encoding scheme is also introduced to reduce the number of parallel processing units. By mapping the pixel values as sign-magnitude numbers and biasing the magnitudes according to their sign bits, eight lattice points in the color space are condensed into one common point density function. Consequently, the same processing element can be used to map several color clusters and the entire FS-SOM network can be substantially scaled down without severely scarifying the quality of the displayed image. The drawback of this encoding scheme is the additional storage ov- - erhead, which can be cut down by leveraging on existing encoder in an overall lossy compression scheme.