A cross-media retrieval method based on deep semantic space, comprising a feature generation stage and a semantic space learning stage. In the feature generation stage, a CNN visual feature vector and an LSTM language description vector of an image are generated by simulating a perception process of a person for the image; and topic information about a text is explored by using an LDA topic model, thus extracting an LDA text topic vector. In the semantic space learning stage, a training set image is trained to obtain a four-layer multi-perception fusion deep neural network, and a training set text is trained to obtain a three-layer text semantic network, respectively. Finally, a test image and a text are respectively mapped to an isomorphic semantic space by using two networks, so as to realize cross-media retrieval. The method can significantly improve the performance of cross-media retrieval, and has a broad application prospect and market demand.
CROSS-MEDIA RETRIEVAL METHOD BASED ON DEEP SEMANTIC SPACE