Discriminative dictionary learning via shared latent structure for object recognition and activity recognition

We propose a novel low-dimensional discriminative dictionary learning approach for multi-class classification tasks, Latent Structure based Discriminative Dictionary Learning (LS-DDL). Our approach first projects features and class labels onto a shared latent structure space, and then generates a discriminative and low-dimensional input to a discriminative dictionary learning framework. LS-DDL learns a more discriminative and lower-dimensional dictionary than existing dictionary learning methods. Therefore we obtain high recognition accuracy with a small number of low-dimensional dictionary atoms. The low dimensionality also improves the efficiency in storage and testing. In addition, the latent structure projection eliminates the classifier weighting parameter in existing discriminative dictionary learning approaches. We validate the effectiveness and efficiency of the proposed approach through a series of experiments on image-based face recognition and video-based activity recognition. Our results show that the proposed approach obtains much higher recognition accuracy with a small number of dictionary atoms, and costs much less computational time than state-of-The-Art methods.