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Discriminative Subsequence Mining for Action Classification


Conference Paper


Recent approaches to action classification in videos have used sparse spatio-temporal words encoding local appearance around interesting movements. Most of these approaches use a histogram representation, discarding the temporal order among features. But this ordering information can contain important information about the action itself, e.g. consider the sport disciplines of hurdle race and long jump, where the global temporal order of motions (running, jumping) is important to discriminate between the two. In this work we propose to use a sequential representation which retains this temporal order. Further, we introduce Discriminative Subsequence Mining to find optimal discriminative subsequence patterns. In combination with the LPBoost classifier, this amounts to simultaneously learning a classification function and performing feature selection in the space of all possible feature sequences. The resulting classifier linearly combines a small number of interpretable decision functions, each checking for the presence of a single discriminative pattern. The classifier is benchmarked on the KTH action classification data set and outperforms the best known results in the literature.

Author(s): Nowozin, S. and BakIr, G. and Tsuda, K.
Book Title: ICCV 2007
Journal: Proceedings of the 11th IEEE International Conference on Computer Vision (ICCV 2007)
Pages: 1919-1923
Year: 2007
Month: October
Day: 0
Publisher: IEEE Computer Society

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1109/ICCV.2007.4409049
Event Name: 11th IEEE International Conference on Computer Vision
Event Place: Rio de Janeiro, Brazil

Address: Los Alamitos, CA, USA
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {Discriminative Subsequence Mining for Action Classification},
  author = {Nowozin, S. and BakIr, G. and Tsuda, K.},
  journal = {Proceedings of the 11th IEEE International Conference on Computer Vision (ICCV 2007)},
  booktitle = {ICCV 2007},
  pages = {1919-1923},
  publisher = {IEEE Computer Society},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Los Alamitos, CA, USA},
  month = oct,
  year = {2007},
  month_numeric = {10}