自动化学报Issue(2)：246-254,9.DOI:10.16383/j.aas.2016.c150105
一种基于广义期望首达时间的形状距离学习算法
A Shape Distance Learning Algorithm Based on Generalized Mean First-passage Time
摘要
Abstract
With the help of shape distance learning introduced into shape matching framework as a post-processing procedure, shape distances obtained by pairwise shape similarity analysis can be improved effectively. A novel shape distance learning method based on generalized mean first-passage time (GMFPT) is proposed to solve the problem of inaccurate matching results caused by mean first-passage time. Given a set of shapes as the state space, the generalized mean first-passage time, which is regarded as the updated shape distance, is used to represent the average time step from one state to a certain set of states. With the generalized mean first-passage time introduced into the distance learning algorithms, context-sensitive similarities can be evaluated effectively, and the shortest paths on the distance manifold can be explicitly captured without redundant context. Simulation experiments are carried out on different shape datasets with the proposed method, and the results demonstrate that the retrieval score can be improved significantly.关键词
形状匹配/形状距离学习/离散时间马尔科夫链/期望首达时间/广义期望首达时间Key words
Shape matching/shape distance learning/discrete-time Markov chain/mean first-passage time/generalized mean first-passage time引用本文复制引用
郑丹晨,杨亚飞,韩敏..一种基于广义期望首达时间的形状距离学习算法[J].自动化学报,2016,(2):246-254,9.基金项目
国家自然科学基金(61374154),中央高校基本科研业务费专项资金(DUT14RC(3)128)资助@@@@Supported by National Natural Science Foundation of China (61374154) and Fundamental Research Funds for the Central Universities (DUT14RC(3)128) （61374154）
