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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, PP. 92-99

Keywords: 形状匹配,形状距离学习,相似度矩阵,离散时间马尔可夫链,期望首达时间

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Abstract:

?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(Meanfirst-passagetime,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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