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  Discriminative Subsequence Mining for Action Classification

Nowozin, S., BakIr, G., & Tsuda, K. (2007). Discriminative Subsequence Mining for Action Classification. In 2007 IEEE 11th International Conference on Computer Vision (pp. 1919-1923). Piscataway, NJ, USA: IEEE Service Center.

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Nowozin, S1, 2, Author           
BakIr, G, Author           
Tsuda, K1, 2, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: 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.

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 Dates: 2007-10
 Publication Status: Published in print
 Pages: -
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 Identifiers: DOI: 10.1109/ICCV.2007.4409049
BibTex Citekey: 4675
 Degree: -

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Title: 11th IEEE International Conference on Computer Vision (ICCV 2007)
Place of Event: Rio de Janeiro, Brazil
Start-/End Date: 2007-10-14 - 2007-10-21

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Title: 2007 IEEE 11th International Conference on Computer Vision
Source Genre: Proceedings
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Publ. Info: Piscataway, NJ, USA : IEEE Service Center
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1919 - 1923 Identifier: ISBN: 978-1-4244-1631-8