Factorization-based texture segmentation
Abstract
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:
-
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- The Ohio State Univ., Columbus, OH (United States). Dept. of Computer Science and Engineering. Center for Cognitive and Brain Sciences
- Publication Date:
- Research Org.:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1286702
- Grant/Contract Number:
- AC05-00OR22725
- Resource 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
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; matrix factorization; spectral histogram; texture segmentation
Citation Formats
Yuan, Jiangye, Wang, Deliang, and Cheriyadat, Anil M. Factorization-based texture segmentation. United States: N. p., 2015.
Web. doi:10.1109/TIP.2015.2446948.
Yuan, Jiangye, Wang, Deliang, & Cheriyadat, Anil M. Factorization-based texture segmentation. United States. https://doi.org/10.1109/TIP.2015.2446948
Yuan, Jiangye, Wang, Deliang, and Cheriyadat, Anil M. Wed .
"Factorization-based texture segmentation". United States. https://doi.org/10.1109/TIP.2015.2446948. https://www.osti.gov/servlets/purl/1286702.
@article{osti_1286702,
title = {Factorization-based texture segmentation},
author = {Yuan, Jiangye and Wang, Deliang and Cheriyadat, Anil M.},
abstractNote = {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.},
doi = {10.1109/TIP.2015.2446948},
journal = {IEEE Transactions on Image Processing},
number = 11,
volume = 24,
place = {United States},
year = {Wed Jun 17 00:00:00 EDT 2015},
month = {Wed Jun 17 00:00:00 EDT 2015}
}
Web of Science
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