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Dynamic Parallel and Distributed Graph Cuts
Yu, Miao1,2; Shen, Shuhan1,3; Hu, Zhanyi1,3,4
Source PublicationIEEE TRANSACTIONS ON IMAGE PROCESSING
2016-12-01
Volume25Issue:12Pages:5511-5525
SubtypeArticle
AbstractGraph cuts are widely used in computer vision. To speed up the optimization process and improve the scalability for large graphs, Strandmark and Kahl introduced a splitting method to split a graph into multiple subgraphs for parallel computation in both shared and distributed memory models. However, this parallel algorithm (the parallel BK-algorithm) does not have a polynomial bound on the number of iterations and is found to be non-convergent in some cases due to the possible multiple optimal solutions of its sub-problems. To remedy this non-convergence problem, in this paper, we first introduce a merging method capable of merging any number of those adjacent sub-graphs that can hardly reach agreement on their overlapping regions in the parallel BK-algorithm. Based on the pseudo-boolean representations of graph cuts, our merging method is shown to be effectively reused all the computed flows in these sub-graphs. Through both splitting and merging, we further propose a dynamic parallel and distributed graph cuts algorithm with guaranteed convergence to the globally optimal solutions within a predefined number of iterations. In essence, this paper provides a general framework to allow more sophisticated splitting and merging strategies to be employed to further boost performance. Our dynamic parallel algorithm is validated with extensive experimental results.
KeywordGraph Cuts Parallel Computation Convergence Markov Random Field
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TIP.2016.2609819
WOS KeywordMAXIMUM-FLOW PROBLEM ; MARKOV RANDOM-FIELDS ; ENERGY MINIMIZATION ; ALGORITHM
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61333015 ; 61421004 ; 61473292)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000388205100001
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/13350
Collection模式识别国家重点实验室_机器人视觉
Affiliation1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Zhongyuan Univ Technol, Zhengzhou 450007, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Yu, Miao,Shen, Shuhan,Hu, Zhanyi. Dynamic Parallel and Distributed Graph Cuts[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2016,25(12):5511-5525.
APA Yu, Miao,Shen, Shuhan,&Hu, Zhanyi.(2016).Dynamic Parallel and Distributed Graph Cuts.IEEE TRANSACTIONS ON IMAGE PROCESSING,25(12),5511-5525.
MLA Yu, Miao,et al."Dynamic Parallel and Distributed Graph Cuts".IEEE TRANSACTIONS ON IMAGE PROCESSING 25.12(2016):5511-5525.
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