Scalable Learning: Probabilistic Graphical Models for Image Analysis

Scientific Achievement

Leveraging advances in mathematics and CS, a new method for graph partitioning scales
image segmentation to run on large DOE HPC platforms, and with high accuracy [1].

Significance and Impact

This new method enables accelerating image
analysis time-to-solution for DOE science
projects challenged by an ever-growing data
tsunami.

Research Results

The method exploits cliques from a Markov Random Field (MRF) formulation, and its solution yields a highly accurate N-label image segmentation

Reduced computational complexity, suitable for use on large problem sizes

Parallel parameter learning for MRF uses LAP strategy for graph partitioning

Shared- and distributed-memory implementations run on DOE HPC platforms

C. Heinemann, T. Perciano, D. Ushizima, and E. W. Bethel, “Distributed Memory Parallel Markov Random Fields Using Graph Partitioning,” in 2017 IEEE International Conference on Big Data (Big Data), pp. 3332–3341, Dec. 2017.

@inproceedings{Heinemann:BigData:2017,
author = {Heinemann, C. and Perciano, T. and Ushizima, D. and Bethel, E. W.},
booktitle = {2017 IEEE International Conference on Big Data (Big Data)},
title = {{Distributed Memory Parallel Markov Random Fields Using Graph Partitioning}},
year = {2017},
pages = {3332--3341},
doi = {10.1109/BigData.2017.8258318},
escholarshipurl = {https://escholarship.org/uc/item/0g13f631},
month = dec
}