ML Anthology

Sequential Sparsification for Change Detection

Necmiye Ozay, Mario Sznaier, Octavia I. Camps
CVPR 2008
doi:10.1109/CVPR.2008.4587473 /cvpr/2008/ozay2008cvpr-sequential/

Abstract

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 l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> 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.

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Cite

Text

Ozay et al. "Sequential Sparsification for Change Detection." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2008. doi:10.1109/CVPR.2008.4587473

Markdown

[Ozay et al. "Sequential Sparsification for Change Detection." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2008.](https://mlanthology.org/cvpr/2008/ozay2008cvpr-sequential/) doi:10.1109/CVPR.2008.4587473

BibTeX

@inproceedings{ozay2008cvpr-sequential,
  title     = {{Sequential Sparsification for Change Detection}},
  author    = {Ozay, Necmiye and Sznaier, Mario and Camps, Octavia I.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2008},
  doi       = {10.1109/CVPR.2008.4587473},
  url       = {https://mlanthology.org/cvpr/2008/ozay2008cvpr-sequential/}
}