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Title: Colour image segmentation using the self-organizing map and adaptive resonance theory
Authors: Yeo, N.C.
Lee, K.H. 
Venkatesh, Y.V. 
Ong, S.H. 
Keywords: Adaptive resonance theory
Colour image segmentation
Lateral control
Network plasticity
Network stability
Neural networks
Self-organizing map
Issue Date: 1-Nov-2005
Citation: Yeo, N.C., Lee, K.H., Venkatesh, Y.V., Ong, S.H. (2005-11-01). Colour image segmentation using the self-organizing map and adaptive resonance theory. Image and Vision Computing 23 (12) : 1060-1079. ScholarBank@NUS Repository.
Abstract: 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. © 2005 Elsevier Ltd. All rights reserved.
Source Title: Image and Vision Computing
ISSN: 02628856
DOI: 10.1016/j.imavis.2005.07.008
Appears in Collections:Staff Publications

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