The Adaptive Subspace Map for Texture Segmentation
نویسندگان
چکیده
In this paper, a non-linear mixture-of-subspaces model is proposed to describe images. Images or image patches, when translated, rotated or scaled, lie in low-dimensional subspaces of the high-dimensional space spanned by the grey values. These manifolds can locally be approximated by a linear subspace. The adaptive subspace map is a method to learn such a mixture-of-subspaces from the data. Due to its general nature, various clustering and subspacefinding algorithms can be used. In this paper, two clustering algorithms are compared in an application to some texture segmentation problems. It is shown to compare well to a standard Gabor filter bank approach.
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