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Optical Engineering

A posteriori multiresolution-based kernel orthogonal subspace technique for supervised texture segmentation
Author(s): G. H. Lee; T. W. Hsieh; J. S. Tsaur; Chin-Wang Tao
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Paper Abstract

We propose an a posteriori kernel orthogonal subspace technique to segment texture images. It is a nonlinear version of the signature subspace classifier (SSC) derived on the basis of an unconstrained least-square estimation. In this approach, the linear subspace mixture model for the SSC is first reformulated in feature space via nonlinear mapping. Then the SSC in feature space is kernelized in terms of the kernel functions so that the dot products in the high dimensional feature space can be implicitly calculated by kernels. The obtained kernel SSC (KSSC) is equivalent to a nonlinear SSC in the input space. After that, the KSSC is applied to segment the texture images. To reduce the computational requirement in segmentation, the multiresolution-based technique (MKSSC) is developed. Experimental results demonstrate that the proposed MKSSC approach can effectively segment texture images and outperforms the MSSC method.

Paper Details

Date Published: 1 July 2008
PDF: 13 pages
Opt. Eng. 47(7) 077006 doi: 10.1117/1.2957049
Published in: Optical Engineering Volume 47, Issue 7
Show Author Affiliations
G. H. Lee, National Chung Hsing Univ. (Taiwan)
T. W. Hsieh, National Chung Hsing Univ. (Taiwan)
J. S. Tsaur, National Chung Hsing Univ. (Taiwan)
Chin-Wang Tao, National Ilan Univ. (Taiwan)

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