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双重分解的并行和分布式图切割

Parallel and Distributed Graph Cuts by Dual Decomposition
课程网址: http://videolectures.net/cvpr2010_strandmark_pdgc/  
主讲教师: Petter Strandmark
开课单位: 隆德大学
开课时间: 2010-07-19
课程语种: 英语
中文简介:
图形切割方法是计算机视觉中许多状态算法的核心,因为它们在计算全局最优解时具有高效率。在本文中,通过将图分成多个部分来并行解决最大流/最小割问题,从而进一步提高图切割的计算效率。通过双重分解保证解决方案的最优性,或者更具体地说,子问题的解决方案在与双变量的重叠上被约束为相等。我们证明我们的方法允许(i)在多核计算机上更快地处理和(ii)处理的能力通过在分布式网络上的多台计算机上拆分图表来解决更大的问题。尽管ourapproach并未给出加速的理论保证,但对具有许多不同数据集的多个应用程序进行广泛的实证评估仍然表现出良好的性能。双分解方法的开源实现也是公开可用的。
课程简介: Graph cuts methods are at the core of many state-of-theart algorithms in computer vision due to their efficiency in computing globally optimal solutions. In this paper, we solve the maximum flow/minimum cut problem in parallel by splitting the graph into multiple parts and hence, further increase the computational efficacy of graph cuts. Optimality of the solution is guaranteed by dual decomposition, or more specifically, the solutions to the subproblems are constrained to be equal on the overlap with dual variables. We demonstrate that our approach both allows (i) faster processing on multi-core computers and (ii) the capability to handle larger problems by splitting the graph across multiple computers on a distributed network. Even though our approach does not give a theoretical guarantee of speedup, an extensive empirical evaluation on several applications with many different data sets consistently shows good performance. An open source implementation of the dual decomposition method is also made publicly available.
关 键 词: 图形切割方法; 计算机视觉; 双重分解
课程来源: 视频讲座网
最后编审: 2019-03-13:lxf
阅读次数: 45