We present an approach to jointly learn a set of viewspecific dictionaries and a common dictionary for crossview action recognition. The set of view-specific dictionaries is learned for specific views while the common dictionary is shared across different views. Our approach represents videos in each view using both the corresponding view-specific dictionary and the common dictionary. More importantly, it encourages the set of videos taken from different views of the same action to have similar sparse representations. In this way, we can align view-specific features in the sparse feature spaces spanned by the viewspecific dictionary set and transfer the view-shared features in the sparse feature space spanned by the common dictionary. Meanwhile, the incoherence between the common dictionary and the view-specific dictionary set enables us to exploit the discrimination information encoded in viewspecific features and view-shared features separately. In addition, the learned common dictionary not only has the capability to represent actions from unseen views, but also makes our approach effective in a semi-supervised setting where no correspondence videos exist and only a few labels exist in the target view. Extensive experiments using the multi-view IXMAS dataset demonstrate that our approach outperforms many recent approaches for cross-view action recognition.
The work REF aligned view-specific features in the sparse feature spaces and transferred the view-shared features in the sparse feature space to maintain a view specific and a common dictionary set, separately.
Learning View-Invariant Sparse Representations for Cross-View Action Recognition
2013 IEEE International Conference on Computer Vision
2013 IEEE International Conference on Computer Vision
Computer Science