Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.imavis.2005.07.008
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
Source: 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. https://doi.org/10.1016/j.imavis.2005.07.008
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
URI: http://scholarbank.nus.edu.sg/handle/10635/55325
ISSN: 02628856
DOI: 10.1016/j.imavis.2005.07.008
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

30
checked on Dec 5, 2017

WEB OF SCIENCETM
Citations

26
checked on Nov 15, 2017

Page view(s)

40
checked on Dec 9, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.