A supervised dictionary learning and discriminative weighting model for action recognition
In this paper, we propose a supervised dictionary learning algorithm for action recognition in still images followed by a discriminative weighting model. The dictionary is learned based on Local Fisher Discrimination which takes into account the local manifold structure and discrimination information of local descriptors. The label information of local descriptors is considered in both dictionary learning and sparse coding stage which generates a supervised sparse coding algorithm and makes the coding coefficients discriminative. Instead of using spatial pyramid features, sliding window-based features with max-pooling are computed from coding coefficients. And then a discriminative weighting model combining a max-margin classifier is proposed using the features. Both the weighting coefficients and model parameters can be jointly learned using the same way in Multiple Kernel Learning algorithm. We validate our model on the following action recognition datasets: Willow 7 human actions dataset, People Playing Music Instrument (PPMI) dataset, and Sports dataset. To show the generality of our model, we also validate it on Scene15 dataset. The experiment results show that only with single scale local descriptors, our algorithm is comparable to some state-of-the-art algorithms.
dictionary learning discriminative weighting model action recognition local fisher discrimination supervised sparse coding multiple kernel learning