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Factorization-based texture segmentation

Journal Article · · IEEE Transactions on Image Processing
 [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
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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.

Research Organization:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
AC05-00OR22725
OSTI ID:
1286702
Journal Information:
IEEE Transactions on Image Processing, Journal Name: IEEE Transactions on Image Processing Journal Issue: 11 Vol. 24; ISSN 1057-7149
Publisher:
IEEECopyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

97 MATHEMATICS AND COMPUTING
matrix factorization
spectral histogram
texture segmentation