DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: 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:
 [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:
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}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Citation Metrics:
Cited by: 64 works
Citation information provided by
Web of Science

Save / Share:

Works referencing / citing this record:

Mutual Information-Based Texture Spectral Similarity Criterion
book, October 2019

  • Haindl, Michal; Havlíček, Michal; Bebis, George
  • Advances in Visual Computing: 14th International Symposium on Visual Computing, ISVC 2019, Lake Tahoe, NV, USA, October 7–9, 2019, Proceedings, Part I, p. 302-314
  • DOI: 10.1007/978-3-030-33720-9_23

Hierarchical coastline detection in SAR images based on spectral-textural features and global–local information
journal, December 2019

  • Modava, Mohammad; Akbarizadeh, Gholamreza; Soroosh, Mohammad
  • IET Radar, Sonar & Navigation, Vol. 13, Issue 12
  • DOI: 10.1049/iet-rsn.2019.0063

Electricity consumption patterns within cities: application of a data-driven settlement characterization method
journal, January 2019

  • Roy Chowdhury, Pranab K.; Weaver, Jeanette E.; Weber, Eric M.
  • International Journal of Digital Earth, Vol. 13, Issue 1
  • DOI: 10.1080/17538947.2018.1556355

Automatic liver tumour segmentation in CT combining FCN and NMF-based deformable model
journal, June 2019

  • Zheng, Shenhai; Fang, Bin; Li, Laquan
  • Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization
  • DOI: 10.1080/21681163.2018.1493618

A Novel Neutrosophic Image Segmentation Based on Improved Fuzzy C -Means Algorithm (NIS-IFCM)
journal, August 2019

  • Zhao, Jing; Wang, Xiaoli; Li, Ming
  • International Journal of Pattern Recognition and Artificial Intelligence, Vol. 34, Issue 05
  • DOI: 10.1142/s0218001420550113

Classifying settlement types from multi-scale spatial patterns of building footprints
journal, May 2020

  • Jochem, Warren C.; Leasure, Douglas R.; Pannell, Oliver
  • Environment and Planning B: Urban Analytics and City Science
  • DOI: 10.1177/2399808320921208

Unsupervised Segmentation of Remote Sensing Images using FD Based Texture Analysis Model and ISODATA
journal, July 2017

  • Hemalatha, S.; Anouncia, S. Margret
  • International Journal of Ambient Computing and Intelligence, Vol. 8, Issue 3
  • DOI: 10.4018/ijaci.2017070104

Unsupervised Segmentation of Remote Sensing Images Using FD Based Texture Analysis Model and ISODATA
book, January 2019