Proceedings of the 9th Joint International Conference on Information Sciences (JCIS-06)

Natural Scene Segmentation Based on Information Fusion and Homogeneity Property

Authors
Heng-Da Cheng 0, Manasi Datar, Wen Ju
Corresponding Author
Heng-Da Cheng
0Computer Science Department, Utah State University
Available Online October 2006.
DOI
https://doi.org/10.2991/jcis.2006.263How to use a DOI?
Keywords
image segmentation, homogeneity, color feature, texture feature, information fusion, HSOM.
Abstract
This paper presents a novel approach to natural scene segmentation. It uses both color and texture features in cooperation to provide comprehensive knowledge about every pixel in the image. A novel scheme for the collection of training samples, based on homogeneity, is proposed. Natural scene segmentation is carried out using a two-stage hierarchical self-organizing map (HSOM). The proposed method confirms that the sample selection based on homogeneity and the self-learning ability and adaptability of the HSOM, coupled with the information fusion mechanism, can lead to good segmentation result, which is validated by experiments on a variety of natural scene images.
Open Access
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Proceedings
9th Joint International Conference on Information Sciences (JCIS-06)
Part of series
Advances in Intelligent Systems Research
Publication Date
October 2006
ISBN
978-90-78677-01-7
ISSN
1951-6851
DOI
https://doi.org/10.2991/jcis.2006.263How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Heng-Da Cheng
AU  - Manasi Datar
AU  - Wen Ju
PY  - 2006/10
DA  - 2006/10
TI  - Natural Scene Segmentation Based on Information Fusion and Homogeneity Property
BT  - 9th Joint International Conference on Information Sciences (JCIS-06)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/jcis.2006.263
DO  - https://doi.org/10.2991/jcis.2006.263
ID  - Cheng2006/10
ER  -