Supervised graph regularization based cross media retrieval with intra and inter-class correlation

Abstract

With the rapid development of internet technology, mining and retrieving the information from internet accurately is an urgent problem, among which, cross media retrieval becomes a hot spot of current research. This paper proposes a cross media retrieval approach, which learns two couples of projections based on different retrieval tasks. We first learn a common subspace to project heterogeneous media data to the isomorphic subspace, to measure the similarity of the heterogeneous media data in the isomorphic subspace. Second, we build isomorphic and heterogeneous adjacent graphs to preserve the correlations of the cross media data. Then we combine the two processes together to learn a common subspace. We also consider intra-class and inter-class similarity of images or texts in the unified framework. Third, the L2 norm is used to perform feature selection for different media data. Experimental results on three datasets demonstrate the effectiveness of the proposed approach.