Please use this identifier to cite or link to this item:
|Title:||Colour image segmentation using the self-organizing map and adaptive resonance theory||Authors:||Yeo, N.C.
|Keywords:||Adaptive resonance theory
Colour image segmentation
|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. 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.
checked on Oct 26, 2020
WEB OF SCIENCETM
checked on Oct 19, 2020
checked on Oct 24, 2020
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.