Our ICIP 2009 and ICCV VOEC 2009 papers are available online. We are working at a novel method based on an effective visual bag-of-words model and on a new spatio-temporal descriptor.

First, we define a new 3D gradient descriptor that combined with optic flow outperforms the state-of-the-art, without requiring fine parameter tuning (ICIP paper).

Second, we show that for spatio-temporal features the popular k-means algorithm is insufficient because cluster centers are attracted by the denser regions of the sample distribution, providing a non-uniform description of the feature space and thus failing to code other informative regions. For this reason we use a radius-based clustering method and a soft assignment that considers the information of two or more relevant candidates, thus obtaining a more effective codebook (ICCV VOEC paper). We extensively test our approach on standard KTH and Weizmann action datasets showing its validity and outperforming other recent approaches.

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