Discriminative Multi-View Subspace Feature Learning for Action Recognition

Although deep features have achieved the state-of-the-art performance in action recognition recently, the hand-crafted shallow features still play a critical role in characterizing human actions for taking advantage of visual contents in an intuitive way such as edge features. Therefore, the shallow features can serve as auxiliary visual cues supplementary to deep representations. In this paper, we propose a discriminative subspace learning model (DSLM) to explore the complementary properties between the hand-crafted shallow feature representations and the deep features. As for the RGB action recognition, this is the first work attempting to mine multi-level feature complementaries by the multi-view subspace learning scheme. To sufficiently capture the complementary information among heterogeneous features, we construct the DSLM by integrating the multi-view reconstruction error and classification error into an unified objective function. To be specific, we first use Fisher Vector to encode improved dense trajectories (iDT+FV) for shallow representations and two-stream convolutional neural network models (T-CNN) for generating deep features. Moreover, the presented DSLM algorithm projects multi-level features onto a shared discriminative subspace with the complementary information and discriminating capacity simultaneously incorporated. Finally, the action types of test samples are identified by the margins from the learned compact representations to the decision boundary. The experimental results on three datasets demonstrate the effectiveness of the proposed method.