NDLI: Towards Efficient and Exact MAP-Inference for Large Scale Discrete Computer Vision Problems via Combinatorial Optimization

Content Provider	IEEE Xplore Digital Library
Author	Kappes, J.H. Speth, M. Reinelt, G. Schnorr, C.
Copyright Year	2013
Description	Author affiliation: Discrete & Combinatorial Optimization Group, Univ. of Heidelberg, Heidelberg, Germany (Speth, M.; Reinelt, G.) \|\| Image & Pattern Anal. Group, Univ. of Heidelberg, Heidelberg, Germany (Kappes, J.H.; Schnorr, C.)
Abstract	Discrete graphical models (also known as discrete Markov random fields) are a major conceptual tool to model the structure of optimization problems in computer vision. While in the last decade research has focused on fast approximative methods, algorithms that provide globally optimal solutions have come more into the research focus in the last years. However, large scale computer vision problems seemed to be out of reach for such methods. In this paper we introduce a promising way to bridge this gap based on partial optimality and structural properties of the underlying problem factorization. Combining these preprocessing steps, we are able to solve grids of size 2048×2048 in less than 90 seconds. On the hitherto unsolvable Chinese character dataset of Nowozin et. al we obtain provably optimal results in 56% of the instances and achieve competitive runtimes on other recent benchmark problems. While in the present work only generalized Potts models are considered, an extension to general graphical models seems to be feasible.
Starting Page	1752
Ending Page	1758
File Size	401830
Page Count	7
File Format	PDF
ISBN	9780769549897
ISSN	10636919
DOI	10.1109/CVPR.2013.229
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Runtime Optimization Computer vision Bridges Polynomials Standards Computational modeling Markov random fields Graphical models discrete optimization
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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