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Title: Factorization-based texture segmentation

This study introduces a factorization-based approach that efficiently segments textured images. We use local spectral histograms as features, and construct an M × N feature matrix using M-dimensional feature vectors in an N-pixel image. Based on the observation that each feature can be approximated by a linear combination of several representative features, we factor the feature matrix into two matrices-one consisting of the representative features and the other containing the weights of representative features at each pixel used for linear combination. The factorization method is based on singular value decomposition and nonnegative matrix factorization. The method uses local spectral histograms to discriminate region appearances in a computationally efficient way and at the same time accurately localizes region boundaries. Finally, the experiments conducted on public segmentation data sets show the promise of this simple yet powerful approach.
Authors:
 [1] ;  [2] ;  [1]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  2. The Ohio State Univ., Columbus, OH (United States). Dept. of Computer Science and Engineering. Center for Cognitive and Brain Sciences
Publication Date:
OSTI Identifier:
1286702
Grant/Contract Number:
AC05-00OR22725
Type:
Accepted Manuscript
Journal Name:
IEEE Transactions on Image Processing
Additional Journal Information:
Journal Volume: 24; Journal Issue: 11; Journal ID: ISSN 1057-7149
Publisher:
IEEE
Research Org:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING matrix factorization; spectral histogram; texture segmentation