Learning to rank biological motion trajectories
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摘要
Many feature transforms have been proposed for the problem of trajectory matching. These methods, which are often based on shape matching, tend to perform poorly for biological trajectories, such as cell motion, because similar biological behavior often results in dissimilar trajectory shape. Additionally, the criteria used for similarity may differ depending on the user's particular interest or the specific query behavior. We present a rank-based distance metric learning method that combines user input and a new set of biologically-motivated features for biological trajectory matching. We show that, with a small amount of user effort, this method outperforms existing trajectory methods. On an information retrieval task using real world data, our method outperforms recent, related methods by ~ 9%.
论文关键词:Biomedical imaging,Motion analysis,Cell motion,Rank-based learning
论文评审过程:Received 16 December 2011, Revised 23 May 2012, Accepted 30 July 2012, Available online 7 August 2012.
论文官网地址:https://doi.org/10.1016/j.imavis.2012.07.010