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ReD-SFA: Relation Discovery Based Slow Feature Analysis for Trajectory Clustering
Zhang, Zhang1,2; Huang, Kaiqi1,2; Tan, Tieniu1,2; Yang, Peipei2; Li, Jun3
2016-06
会议名称The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
会议录名称The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016
会议日期2016-6
会议地点Las Vegas, USA
摘要
For spectral embedding/clustering, it is still an open problem on how to construct an relation graph to reflect the intrinsic structures in data. In this paper, we proposed an approach, named Relation Discovery based Slow Feature Analysis (ReD-SFA), for feature learning and graph construction simultaneously. Given an initial graph with only a few nearest but most reliable pairwise relations, new reliable relations are discovered by an assumption of reliability preservation, i.e., the reliable relations will preserve their reliabilities in the learnt projection subspace. We formulate the idea as a cross entropy (CE) minimization problem to reduce the discrepancy between two Bernoulli distributions parameterized by the updated distances and the existing relation graph respectively. Furthermore, to overcome the imbalanced distribution of samples, a Boosting-like strategy is proposed to balance the discovered relations over all clusters. To evaluate the proposed method, extensive experiments are performed with various trajectory clustering tasks, including motion segmentation, time series clustering and crowd detection. The results demonstrate that ReD-SFA can discover reliable intra-cluster relations with high precision, and competitive clustering performance can be achieved in comparison with state-of-the-art.
关键词Slow Feature Analysis Trajectory Clustering
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/12504
专题智能感知与计算研究中心
通讯作者Zhang, Zhang
作者单位1.Center for Research on Intelligent Perception and Computing
2.National Laboratory of Pattern Recognition
3.University of Technology, Sydney
推荐引用方式
GB/T 7714
Zhang, Zhang,Huang, Kaiqi,Tan, Tieniu,et al. ReD-SFA: Relation Discovery Based Slow Feature Analysis for Trajectory Clustering[C],2016.
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