In this paper, we propose a novel information theoretic approach to obtain compact and discriminative dictionary of visual data. This approach squeezes discriminative information from the dictionary for efficient representation using information bottleneck. The dictionary is optimized from the initial sparse dictionary, which is learned from action data. In this, a constraint information optimization problem is formulated in which mutual information between the initial and optimized dictionary is minimized while maximizing mutual information between optimized dictionary and class labels. We use an effective similarity measure, Jensen-Shannon divergence with adaptive weightages, for class distributions of each dictionary atom. These adaptive weightages are obtained based on the usage of the dictionary atom among different classes. The resultant dictionary becomes discriminative and compact, while retaining maximum information with fewer atoms. Using simple reconstruction error, we test computational efficiency of the proposed method without compromising classification accuracy on popular benchmark datasets. It is further demonstrated how efficiently discriminative information is retained by comparing the classification performance of the dictionary before and after the removal of redundant dictionary atoms.

In this letter, we propose sparsity-based coherent and noncoherent dictionaries for action recognition. First, the input data are divided into different clusters and the number of clusters depends on the number of action categories. Within each cluster, we seek data items of each action category. If the number of data items exceeds threshold in any action category, these items are labeled as coherent. In a similar way, all coherent data items from different clusters form a coherent group of each action category, and data that are not part of the coherent group belong to noncoherent group of each action category. These coherent and noncoherent groups are learned using K-singular value decomposition dictionary learning. Since the coherent group has more similarity among data, only few atoms need to be learned. In the noncoherent group, there is a high variability among the data items. So, we propose an orthogonal-projection-based selection to get optimal dictionary in order to retain maximum variance in the data. Finally, the obtained dictionary atoms of both groups in each action category are combined and then updated using the limited Broyden-Fletcher-Goldfarb-Shanno optimization algorithm. The experiments are conducted on challenging datasets HMDB51 and UCF50 with action bank features and achieve comparable result using this state-of-the-art feature.