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Improved Optimization Based on Graph Cuts for Discrete Energy Minimization
Liu KW(刘康伟); Zhang JG(张俊格); Huang KQ(黄凯奇); Huang KQ(黄凯奇)
Conference Name2014 22nd International Conference on Pattern Recognition
Source PublicationInternational Conference on Pattern Recognition
Conference Date2014
Conference Place瑞典
Discrete energy optimization is a NP hard problem. Recent years, the graph cuts based algorithms especially the α-expansion and αβ-swap, become more and more popular. Both the α-expansion and αβ-swap have been widely used in many applications, and they perform extremely well for the Potts energies. However, since all pixels only have a choice of two labels in one move, both the expansion and swap algorithm get approximate solution by a series of iterations and they do not perform well for more general energies, such as the truncated convex energies [1]. In this paper, we analyze the problems of both
the expansion and swap algorithms. The expansion algorithm usually encourages more pixels to get the label fα, since all pixels are only allowed to change their current labels to fα. In contrast, the swap move sometimes cannot swap the labels of pixels reasonably. Based on the analysis, we propose the Interleaved Expansion-Swap Algorithm (IESA) by combining the expansion and swap moves effectively. To prove the effectiveness of the algorithm, we test it on both image restoration and stereo correspondence. The experimental evaluations show that our algorithm gets better optimization compared with both α-expansion and αβ-swap.
Other Abstract
物体视觉结构模型研究中另外一个非常重要的问题是如何对结构模型进行快速高效地推理。通常,结构模型的推理问题可以转化为离散能量优化的问题。近年来,基于图割的推理算法尤其是 $\alpha$-expansion 和 $\alpha\beta$-swap 算法在很多视觉任务中得到了广泛应用。对于 Potts 能量函数的优化问题,上述两种算法都得到令人满意的结果。然而,由于这两种算法都是通过一系列的迭代优化过程来对随机场模型进行推理,它们都只能得到近似的解,且在一般的能量函数(例如截凸函数~\cite{RangeSwap07})上无法得到非常好的推理效果。我们详细地分析了 $\alpha$-expansion 和 $\alpha\beta$-swap 算法的优缺点。其中,由于 $\alpha$-expansion 标号移动允许所有节点将其当前标号改变为另一标号$f_\alpha$,因此 $\alpha$-expansion 算法通常使得相邻的节点都得到相同的标号。相对地,$\alpha\beta$-swap 算法往往无法合理地交换节点间的标号。基于这个分析,我们通过有效地结合 $\alpha$-expansion 和 $\alpha\beta$-swap 算法各自的优势,提出了\emph{交互的扩展-交换算法}(Interleaved Expansion-Swap Algorithm,IESA)。为了证明算法的有效性,我们同时在图像去噪和立体匹配问题上对其进行测试,实验结果表明 IESA 算法得到更好的推理结果。
Indexed ByEI
Document Type会议论文
Corresponding AuthorHuang KQ(黄凯奇)
Recommended Citation
GB/T 7714
Liu KW,Zhang JG,Huang KQ,et al. Improved Optimization Based on Graph Cuts for Discrete Energy Minimization[C],2014.
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