Semantic Discriminative Projections for Image Retrieval

Subspace learning has attracted much attention in image retrieval. In this paper, we present a subspace learning approach called Semantic Discriminative Projections (SDP), which learns the semantic subspace through integrating the descriptive information and discriminative information. We first construct one graph to characterize the similarity of contented-based features, another to describe the semantic dissimilarity. Then we formulate constrained optimization problem with a penalized difference form. Therefore, we can avoid the singularity problem and get the optimal dimensionality while learning a semantic subspace. Furthermore, SDP may be conducted in the original space or in the reproducing kernel Hilbert space into which images are mapped. This gives rise to kernel SDP. We investigate extensive experiments to verify the effectiveness of our approach. Experimental results show that our approach achieves better retrieval performance than state-of-art methods.