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Proceedings Paper

Unsupervised texture segmentation based on nonsubsampled contourlet and a novel artificial immune network
Author(s): Wenlong Huang; Licheng Jiao
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Paper Abstract

This paper describes a novel structural adaptation artificial immune network (SAAN) clustering algorithm for texture segmentation. In the SAAN, a new immune antibody neighborhood and an adaptive learning coefficient are presented. The model can adaptively map input data into the antibody output space, which has a better adaptive net structure. Images are first partitioned into a set of regions by using the watershed segmentation. Then the nonsubsampled contourlet texture features are extracted from each watershed region as the antigens of the SAAN. Finally the antibodies clustering results of the SAAN are combined to yield a global clustering solution by the minimal spanning tree, which need not a predefined number of clustering. The experimental results with various texture images illustrate the effectiveness of the proposed novel segmentation algorithm.

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, 678624 (15 November 2007); doi: 10.1117/12.749347
Show Author Affiliations
Wenlong Huang, Xidian Univ. (China)
Licheng Jiao, Xidian Univ. (China)


Published in SPIE Proceedings Vol. 6786:
MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition

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