A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
Color Image Segmentation using Competitive Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Comparative analysis of fuzzy ART and ART-2A network clustering performance
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Hierarchical SOMs: Segmentation of Cell-Migration Images
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
A New Type of ART2 Architecture and Application to Color Image Segmentation
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Review article: Local adaptive receptive field self-organizing map for image color segmentation
Image and Vision Computing
Color reduction using a multi-stage Kohonen Self-Organizing Map with redundant features
Expert Systems with Applications: An International Journal
Color image segmentation using parallel OptiMUSIG activation function
Applied Soft Computing
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We propose a new competitive-learning neural network model for colour image segmentation. The model, which is based on the adaptive resonance theory (ART) of Carpenter and Grossberg and on the self-organizing map (SOM) of Kohonen, overcomes the limitations of (i) the stability-plasticity trade-offs in neural architectures that employ ART; and (ii) the lack of on-line learning property in the SOM. In order to explore the generation of a growing feature map using ART and to motivate the main contribution, we first present a preliminary experimental model, SOMART, based on Fuzzy ART. Then we propose the new model, SmART, that utilizes a novel lateral control of plasticity to resolve the stability-plasticity problem. SmART has been experimentally found to perform well in RGB colour space, and is believed to be more coherent than Fuzzy ART.