Please use this identifier to cite or link to this item:
https://doi.org/10.1016/S0262-8856(02)00021-5
DC Field | Value | |
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dc.title | Segmentation of color images using a two-stage self-organizing network | |
dc.contributor.author | Ong, S.H. | |
dc.contributor.author | Yeo, N.C. | |
dc.contributor.author | Lee, K.H. | |
dc.contributor.author | Venkatesh, Y.V. | |
dc.contributor.author | Cao, D.M. | |
dc.date.accessioned | 2014-06-17T03:05:09Z | |
dc.date.available | 2014-06-17T03:05:09Z | |
dc.date.issued | 2002-04-01 | |
dc.identifier.citation | 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 | |
dc.identifier.issn | 02628856 | |
dc.identifier.uri | http://scholarbank.nus.edu.sg/handle/10635/57349 | |
dc.description.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. | |
dc.description.uri | http://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/S0262-8856(02)00021-5 | |
dc.source | Scopus | |
dc.subject | Artificial neural network | |
dc.subject | Color clustering | |
dc.subject | Color image segmentation | |
dc.subject | Self-organizing map | |
dc.type | Article | |
dc.contributor.department | MECHANICAL ENGINEERING | |
dc.contributor.department | ELECTRICAL & COMPUTER ENGINEERING | |
dc.description.doi | 10.1016/S0262-8856(02)00021-5 | |
dc.description.sourcetitle | Image and Vision Computing | |
dc.description.volume | 20 | |
dc.description.issue | 4 | |
dc.description.page | 279-289 | |
dc.description.coden | IVCOD | |
dc.identifier.isiut | 000175110100003 | |
Appears in Collections: | Staff Publications |
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