skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Distributed memory parallel Markov random fields using graph partitioning

Abstract

Markov random fields (MRF) based algorithms have attracted a large amount of interest in image analysis due to their ability to exploit contextual information about data. Image data generated by experimental facilities, though, continues to grow larger and more complex, making it more difficult to analyze in a reasonable amount of time. Applying image processing algorithms to large datasets requires alternative approaches to circumvent performance problems. Aiming to provide scientists with a new tool to recover valuable information from such datasets, we developed a general purpose distributed memory parallel MRF-based image analysis framework (MPI-PMRF). MPI-PMRF overcomes performance and memory limitations by distributing data and computations across processors. The proposed approach was successfully tested with synthetic and experimental datasets. Additionally, the performance of the MPI-PMRF framework is analyzed through a detailed scalability study. We show that a performance increase is obtained while maintaining an accuracy of the segmentation results higher than 98%. The contributions of this paper are: (a) development of a distributed memory MRF framework; (b) measurement of the performance increase of the proposed approach; (c) verification of segmentation accuracy in both synthetic and experimental, real-world datasets

Authors:
 [1];  [2];  [3];  [1]
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Computational Research Div.; Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Center for Advanced Mathematics for Energy Research Applications (CAMERA)
  3. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Computational Research Div.; Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Center for Advanced Mathematics for Energy Research Applications (CAMERA)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
OSTI Identifier:
1440002
DOE Contract Number:  
AC02-05CH11231
Resource Type:
Conference
Resource Relation:
Conference: 2017 IEEE International Conference on Big Data, Seattle, WA (United States), 11-14 Dec 2017
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Heinemann, C., Perciano, T., Ushizima, D., and Bethel, E. W. Distributed memory parallel Markov random fields using graph partitioning. United States: N. p., 2017. Web. doi:10.1109/BigData.2017.8258318.
Heinemann, C., Perciano, T., Ushizima, D., & Bethel, E. W. Distributed memory parallel Markov random fields using graph partitioning. United States. doi:10.1109/BigData.2017.8258318.
Heinemann, C., Perciano, T., Ushizima, D., and Bethel, E. W. Fri . "Distributed memory parallel Markov random fields using graph partitioning". United States. doi:10.1109/BigData.2017.8258318. https://www.osti.gov/servlets/purl/1440002.
@article{osti_1440002,
title = {Distributed memory parallel Markov random fields using graph partitioning},
author = {Heinemann, C. and Perciano, T. and Ushizima, D. and Bethel, E. W.},
abstractNote = {Markov random fields (MRF) based algorithms have attracted a large amount of interest in image analysis due to their ability to exploit contextual information about data. Image data generated by experimental facilities, though, continues to grow larger and more complex, making it more difficult to analyze in a reasonable amount of time. Applying image processing algorithms to large datasets requires alternative approaches to circumvent performance problems. Aiming to provide scientists with a new tool to recover valuable information from such datasets, we developed a general purpose distributed memory parallel MRF-based image analysis framework (MPI-PMRF). MPI-PMRF overcomes performance and memory limitations by distributing data and computations across processors. The proposed approach was successfully tested with synthetic and experimental datasets. Additionally, the performance of the MPI-PMRF framework is analyzed through a detailed scalability study. We show that a performance increase is obtained while maintaining an accuracy of the segmentation results higher than 98%. The contributions of this paper are: (a) development of a distributed memory MRF framework; (b) measurement of the performance increase of the proposed approach; (c) verification of segmentation accuracy in both synthetic and experimental, real-world datasets},
doi = {10.1109/BigData.2017.8258318},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri Dec 01 00:00:00 EST 2017},
month = {Fri Dec 01 00:00:00 EST 2017}
}

Conference:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share: