Please use this identifier to cite or link to this item: https://doi.org/10.1016/S0262-8856(02)00021-5
Title: Segmentation of color images using a two-stage self-organizing network
Authors: Ong, S.H. 
Yeo, N.C.
Lee, K.H. 
Venkatesh, Y.V. 
Cao, D.M.
Keywords: Artificial neural network
Color clustering
Color image segmentation
Self-organizing map
Issue Date: 1-Apr-2002
Source: Ong, S.H.,Yeo, N.C.,Lee, K.H.,Venkatesh, Y.V.,Cao, D.M. (2002-04-01). Segmentation of color images using a two-stage self-organizing network. Image and Vision Computing 20 (4) : 279-289. ScholarBank@NUS Repository. https://doi.org/10.1016/S0262-8856(02)00021-5
Abstract: We propose a two-stage hierarchical artificial neural network for the segmentation of color images based on the Kohonen self-organizing map (SOM). The first stage of the network employs a fixed-size two-dimensional feature map that captures the dominant colors of an image in an unsupervised mode. The second stage combines a variable-sized one-dimensional feature map and color merging to control the number of color clusters that is used for segmentation. A post-processing noise-filtering stage is applied to improve segmentation quality. Experiments confirm that the self-learning ability, fault tolerance and adaptability of the two-stage SOM lead to a good segmentation results. © 2002 Elsevier Science B.V. All rights reserved.
Source Title: Image and Vision Computing
URI: http://scholarbank.nus.edu.sg/handle/10635/57349
ISSN: 02628856
DOI: 10.1016/S0262-8856(02)00021-5
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