Annotating And Retrieving Videos Of Human Actions Using Matrix Factorization

This paper presents a method for annotating and retrieving videos of human actions based on two-way matrix factorization. The method addresses the problem by modeling it as the problem of finding common latent space representation for multimodal objects. In this particular case, the modalities correspond to the visual and textual (annotations) information associated with videos, which are projected by the method to the latent space. Assuming this space exists, it is possible to map between input spaces, i.e. visual to textual, by projecting across the latent space. The mapping between the spaces is explicitly optimized in the cost function and learned from training data including both modalities. The algorithm may be used for annotation, by projecting only visual information and obtaining a textual representation, or for retrieval by indexing on the latent or textual spaces. Experimental evaluation shows competitive results when compared to state-of-the-art annotation and retrieval methods.