TY - GEN
T1 - Subspace Clustering for Sequential Data
AU - Tierney, Stephen
AU - Gao, Junbin
AU - Guo, Yi
N1 - Imported on 03 May 2017 - DigiTool details were: publisher = USA: IEEE Xplore, 2014. Event dates (773o) = 23-28 June 2014; Parent title (773t) = Conference on Computer Vision and Pattern Recognition. ISSNs: 1063-6919;
PY - 2014
Y1 - 2014
N2 - We propose Ordered Subspace Clustering (OSC) to segment data drawn from a sequentially ordered union of subspaces. Current subspace clustering techniques learn the relationships within a set of data and then use a separate clustering algorithm such as NCut for final segmentation. In contrast our technique, under certain conditions, is capable of segmenting clusters intrinsically without providing the number of clusters as a parameter. Similar to Sparse Subspace Clustering (SSC) we formulate the problem as one of finding a sparse representation but include a new penalty term to take care of sequential data. We test our method on data drawn from infrared hyper spectral data, video sequences and face images. Our experiments show that our method, OSC, outperforms the state of the art methods: Spatial Subspace Clustering (SpatSC), Low-Rank Representation (LRR) and SSC.
AB - We propose Ordered Subspace Clustering (OSC) to segment data drawn from a sequentially ordered union of subspaces. Current subspace clustering techniques learn the relationships within a set of data and then use a separate clustering algorithm such as NCut for final segmentation. In contrast our technique, under certain conditions, is capable of segmenting clusters intrinsically without providing the number of clusters as a parameter. Similar to Sparse Subspace Clustering (SSC) we formulate the problem as one of finding a sparse representation but include a new penalty term to take care of sequential data. We test our method on data drawn from infrared hyper spectral data, video sequences and face images. Our experiments show that our method, OSC, outperforms the state of the art methods: Spatial Subspace Clustering (SpatSC), Low-Rank Representation (LRR) and SSC.
U2 - 10.1109/CVPR.2014.134
DO - 10.1109/CVPR.2014.134
M3 - Conference paper
SP - 1019
EP - 1026
BT - CVPR 2014
PB - IEEE Xplore
CY - USA
T2 - Conference on Computer Vision and Pattern Recognition
Y2 - 23 June 2014 through 28 June 2014
ER -