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基于期望首达时间的形状距离学习算法

郑丹晨 韩敏

郑丹晨, 韩敏. 基于期望首达时间的形状距离学习算法. 自动化学报, 2014, 40(1): 92-99. doi: 10.3724/SP.J.1004.2014.00092
引用本文: 郑丹晨, 韩敏. 基于期望首达时间的形状距离学习算法. 自动化学报, 2014, 40(1): 92-99. doi: 10.3724/SP.J.1004.2014.00092
ZHENG Dan-Chen, HAN Min. Learning Shape Distance Based on Mean First-passage Time. ACTA AUTOMATICA SINICA, 2014, 40(1): 92-99. doi: 10.3724/SP.J.1004.2014.00092
Citation: ZHENG Dan-Chen, HAN Min. Learning Shape Distance Based on Mean First-passage Time. ACTA AUTOMATICA SINICA, 2014, 40(1): 92-99. doi: 10.3724/SP.J.1004.2014.00092

基于期望首达时间的形状距离学习算法

doi: 10.3724/SP.J.1004.2014.00092
基金项目: 

国家自然科学基金(61374154,61074096)资助

Learning Shape Distance Based on Mean First-passage Time

Funds: 

Supported by National Natural Science Foundation of China (61374154, 61074096)

  • 摘要: 由于逐对形状匹配不能很好地反映形状间相似度,因此需要引入后期处理步骤提升检索精度. 为了得到上下文敏感的形状相似度,本文提出了一种基于期望首达时间(Mean first-passage time,MFPT)的形状距离学习方法. 在利用标准形状匹配方法得到距离矩阵的基础上,建立离散时间马尔可夫链对形状流形结构进行分析.将形状样本视作状态,利用不同状态之间完成一次状态转移的平均时间步长,即期望首达时间,表示形状间的距离.期望首达时间能够结合测地距离发掘空间流形结构,并可以通过线性方程进行有效求解.分别对不同数据进行实验分析,本文所提出的方法在相同条件下能够达到更高的形状检索精度.
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出版历程
  • 收稿日期:  2012-04-27
  • 修回日期:  2012-09-29
  • 刊出日期:  2014-01-20

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