Sciweavers

Share
CVPR
2008
IEEE

Sequential sparsification for change detection

12 years 7 months ago
Sequential sparsification for change detection
This paper presents a general method for segmenting a vector valued sequence into an unknown number of subsequences where all data points from a subsequence can be represented with the same affine parametric model. The idea is to cluster the data into the minimum number of such subsequences which, as we show, can be cast as a sparse signal recovery problem by exploiting the temporal correlation between consecutive data points. We try to maximize the sparsity (i.e. the number of zero elements) of the first order differences of the sequence of parameter vectors. Each non-zero element in the first order difference sequence corresponds to a change. A weighted l1 norm based convex approximation is adopted to solve the change detection problem. We apply the proposed method to video segmentation and temporal segmentation of dynamic textures.
Necmiye Ozay, Mario Sznaier, Octavia I. Camps
Added 12 Oct 2009
Updated 28 Oct 2009
Type Conference
Year 2008
Where CVPR
Authors Necmiye Ozay, Mario Sznaier, Octavia I. Camps
Comments (0)
books