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Proceedings Paper

Improved clustering algorithm for image segmentation based on CSA
Author(s): Xiaohua Zhang; Pu Yang; Licheng Jiao; Xiaojin Hou
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Paper Abstract

Image segmentation is the prerequisite step for further image analysis. Segmentation algorithms based on clustering attract more and more attentions. In this paper, an image-domain based clustering method for segmentation, called CSA-CA, is proposed. In this method, a scale parameter is introduced instead of an apriori known number of clusters. Considering that adjacent pixels are generally not independent of each other, the spatial local context is took account into our method. A spatial information term is added so that the near pixels have higher probability to merge into one cluster. Additionally, a clonal selection clustering operator is used so that a cluster is capable of exploring the others that are not neighboring in spatial but similar in feature. In the experiments we show the effectiveness of the proposed method and compare it to other segmentation algorithms.

Paper Details

Date Published: 15 November 2007
PDF: 7 pages
Proc. SPIE 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition, 67863S (15 November 2007); doi: 10.1117/12.750466
Show Author Affiliations
Xiaohua Zhang, Xidian Univ. (China)
Pu Yang, Xidian Univ. (China)
Licheng Jiao, Xidian Univ. (China)
Xiaojin Hou, Xidian Univ. (China)

Published in SPIE Proceedings Vol. 6786:
MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition
Tianxu Zhang; Tianxu Zhang; Carl Anthony Nardell; Carl Anthony Nardell; Hanqing Lu; Duane D. Smith; Hangqing Lu, Editor(s)

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