DocumentCode :
3748935
Title :
Temporal Subspace Clustering for Human Motion Segmentation
Author :
Sheng Li;Kang Li;Yun Fu
fYear :
2015
Firstpage :
4453
Lastpage :
4461
Abstract :
Subspace clustering is an effective technique for segmenting data drawn from multiple subspaces. However, for time series data (e.g., human motion), exploiting temporal information is still a challenging problem. We propose a novel temporal subspace clustering (TSC) approach in this paper. We improve the subspace clustering technique from two aspects. First, a temporal Laplacian regularization is designed, which encodes the sequential relationships in time series data. Second, to obtain expressive codings, we learn a non-negative dictionary from data. An efficient optimization algorithm is presented to jointly learn the representation codings and dictionary. After constructing an affinity graph using the codings, multiple temporal segments can be grouped via spectral clustering. Experimental results on three action and gesture datasets demonstrate the effectiveness of our approach. In particular, TSC significantly improves the clustering accuracy, compared to the state-of-the-art subspace clustering methods.
Keywords :
"Dictionaries","Encoding","Laplace equations","Time series analysis","Clustering methods","Computer vision","Motion segmentation"
Publisher :
ieee
Conference_Titel :
Computer Vision (ICCV), 2015 IEEE International Conference on
Electronic_ISBN :
2380-7504
Type :
conf
DOI :
10.1109/ICCV.2015.506
Filename :
7410863
Link To Document :
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