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A Message Passing Algorithm for MRF Inference with Unknown Graphs and Its Applications

  • Zhenhua Wang
  • Zhiyi ZhangEmail author
  • Nan Geng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9006)

Abstract

Recent research shows that estimating labels and graph structures simultaneously in Markov random Fields can be achieved via solving LP problems. The scalability is a bottleneck that prevents applying such technique to larger problems such as image segmentation and object detection. Here we present a fast message passing algorithm based on the mixed-integer bilinear programming formulation of the original problem. We apply our algorithm to both synthetic data and real-world applications. It compares favourably with previous methods.

Notes

Acknowledgement

We thank Qinfeng Shi for his suggestion on the exposition of this paper. We thank Cesar Dario Cadena Lerma for his help on using the KITTI dataset. This work was supported by a grant from the National High Technology Research and Development Program of China (863 Program) (No. 2013AA10230402), and a grant from the Fundamental Research Funds of Northwest A&F University (No. QN2013056).

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.School of Computer ScienceThe University of AdelaideAdelaideAustralia
  2. 2.College of Information EngineeringNorthwest A&F UniversityYangling DistrictChina

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