2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2014)
Columbus, OH, USA
June 23, 2014 to June 28, 2014
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CVPR.2014.134
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.
Zirconium, Face, PSNR, Clustering algorithms, Minerals, Data models
S. Tierney, J. Gao and Y. Guo, "Subspace Clustering for Sequential Data," 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA, 2014, pp. 1019-1026.