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|標題:||A posteriori multiresolution-based kernel orthogonal subspace technique for supervised texture segmentation||作者:||Lee, G.H.
|關鍵字:||texture segmentation;signature subspace classifier;fuzzy texture;spectrum;projection approach;neural-networks;image classification||Project:||Optical Engineering||期刊/報告no：:||Optical Engineering, Volume 47, Issue 7.||摘要:||
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. (C) 2008 Society of Photo-Optical Instrumentation Engineers.
|Appears in Collections:||電機工程學系所|
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