The fundamental issue of multi-modality information cross-retrieval is feature representation of multi-modality data. Sparse coding is an effective representation method for feature modeling. However

when the query terms and the retrieval terms come from different modalities

the traditional sparse coding may never be suitable because the distribution difference between different modalities and similar features can be encoded as a significant difference of sparse representation. Therefore

in this paper

we present a multi-modality information cross-retrieval algorithm based on sparse coding. In the proposed method

maximum mean difference (MMD) and graph Laplacianare used to formulate the sparse coding objective function to thoroughly exploit the multimodal information in coding. Then

feature-sign search and discrete line search algorithm are used to optimize the objective function. We performed a cross-retrieval experiment on a Wikipedia text-image dataset and compared the proposed method with traditional sparse coding methods. The experimental result shows that the proposed method increased the average mean average precision (MAP) of cross-retrieval by 18.7%. The proposed algorithm improves the robustness of sparse coding and the accuracy of multimodal cross-retrieval. and more suitable for extracting features of multimodality data for further operations

such as cross-retrieval

classification

etc.