CASIA OpenIR  > 智能感知与计算研究中心
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
Conference NameThe IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Source PublicationThe IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016
Conference Date2016-6
Conference PlaceLas Vegas, USA
Abstract
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.
KeywordSlow Feature Analysis Trajectory Clustering
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12504
Collection智能感知与计算研究中心
Corresponding AuthorZhang, Zhang
Affiliation1.Center for Research on Intelligent Perception and Computing
2.National Laboratory of Pattern Recognition
3.University of Technology, Sydney
Recommended Citation
GB/T 7714
Zhang, Zhang,Huang, Kaiqi,Tan, Tieniu,et al. ReD-SFA: Relation Discovery Based Slow Feature Analysis for Trajectory Clustering[C],2016.
Files in This Item: Download All
File Name/Size DocType Version Access License
Zhang_ReD-SFA_Relati(1177KB)会议论文 开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Zhang, Zhang]'s Articles
[Huang, Kaiqi]'s Articles
[Tan, Tieniu]'s Articles
Baidu academic
Similar articles in Baidu academic
[Zhang, Zhang]'s Articles
[Huang, Kaiqi]'s Articles
[Tan, Tieniu]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Zhang, Zhang]'s Articles
[Huang, Kaiqi]'s Articles
[Tan, Tieniu]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: Zhang_ReD-SFA_Relation_Discovery_CVPR_2016_paper.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.