Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Self-organizing maps
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
A self-organising network that grows when required
Neural Networks - New developments in self-organizing maps
Engineering Applications of Artificial Intelligence
Sample-size adaptive self-organization map for color images quantization
Pattern Recognition Letters
Colour image segmentation using the self-organizing map and adaptive resonance theory
Image and Vision Computing
Novel fast color reduction algorithm for time-constrained applications
Journal of Visual Communication and Image Representation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Neural networks for vector quantization of speech and images
IEEE Journal on Selected Areas in Communications
New adaptive color quantization method based on self-organizing maps
IEEE Transactions on Neural Networks
Color clustering and learning for image segmentation based on neural networks
IEEE Transactions on Neural Networks
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
Color histogram-based image segmentation
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Image segmentation using fuzzy logic, neural networks and genetic algorithms: survey and trends
Machine Graphics & Vision International Journal
Self-organizing maps with a time-varying structure
ACM Computing Surveys (CSUR)
Hierarchical Color Quantization Based on Self-organization
Journal of Mathematical Imaging and Vision
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A new self-organizing map with variable topology is introduced for image segmentation. The proposed network, called a Local Adaptive Receptive Field Self-organizing Map (LARFSOM), is a fast convergent network capable of color segmenting images satisfactorily, which has optimum self-adaptive topology and achieves good PSNR values. LARFSOM is compared to SOM, FS-SOM and GNG, self-organizing maps used for color segmentation. LARFSOM reached a higher color palette variance and a better 3D RGB color space distribution of learned data from the training images than the other models. LARFSOM was tested to segment images with different degrees of complexity and has given promising results.