Discriminative Subsequence Mining for Action Classification

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 functio...

• Type: Conference
• Year: 2007
• Where: ICCV
• Authors: Sebastian Nowozin, Gökhan H. Bakir, Koji Tsuda
• Keywords: Computer Vision, Discriminative Subsequence Mining, Global Temporal Order, ICCV 2007, Optimal Discriminative Subsequence, Single Discriminative Pattern, Temporal Order