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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.