A cross-modal retrieval method comprises: using a stacked restricted Boltzmann machine and a multimodal deep belief network to extract a modal friendly feature and a modal interactive feature of each of an image and text, wherein the modal friendly feature causes statistical characteristics between the acquired features to be more similar to input characteristics, and the modal interactive feature acquires mutual information lost in an original input instance; combining the two features to acquire a mixed feature; and acquiring a final shared feature by means of multiple bi-modal automatic encodings. In the method, the stacked restricted Boltzmann machine is used to extract internal features of each modality, and the deep belief network is used to mine lost mutual information between modalities; the two features are combined to construct a mixed feature suitable for cross-modal retrieval, and a multi-layer bi-modal automatic encoding network is used to mine cross-modal complex information, thereby effectively improving the accuracy and retrieval speed of a cross-modal retrieval task.
CROSS-MODAL RETRIEVAL METHOD AND SYSTEM