LAUSR

On the network, a large amount of multi-modal data has emerged. Efficiently utilizing such data to conduct cross modal retrieval has become a hot topic of research. Some solutions have been proposed for this problem. However, many of these methods only considered the local structural information, thus losing sight of the global structural information of data. To overcome this problem and enhance retrieval accuracy, we propose a multi-modal graph regularization based class center discriminant analysis for cross modal retrieval. The core of our method is to maximize the intra-modality distance and minimize the inter-modality distance of class center samples to strengthen the discriminant ability of the model. Meanwhile, a multi-modal graph, which consists of the inter-modality similarity graph, the class center intra-modality graph and the inter-modality graph, is fused into the method to further reinforce the semantic similarity between different modalities. The method considers the local structural information of data together with the global structural information of data. Experimental results on three benchmark datasets demonstrate the superiority of this proposed scheme over several state-of-the-art methods.