Learning image Vicept description via mixed-norm regularization for large scale semantic image search

The paradox of visual polysemia and concept polymorphism has been a great challenge in the large scale semantic image search. To address this problem, our paper proposes a new method to generate image Vicept representation. Vicept characterizes the membership distribution between elementary visual appearances and semantic concepts, and forms a hierarchical representation of image semantic from local to global. To obtain discriminative Vicept descriptions with structural sparsity, we adopt mixed-norm regularization in the optimization problem for learning the concept membership distribution of visual word. Furthermore, considering the structure of BOV in images, visual descriptor is encoded as a weighted sum of dictionary elements using group sparse coding, which could obtain sparse representation at the image level. The wide applications of Vicept are validated in our experiments, including large scale semantic image search, image annotation, and semantic image re-ranking.