A cross-modal multimedia retrieval method using depth correlation mining in big data environment

Abstract

Cross-media retrieval is a technology aimed at breaking through the shackles of single-mode retrieval technology, which is limited to the same multimedia form. It is also hoped to be able to search each other across the media form. Comprehensive processing of different multimedia morphological data is an urgent problem to be solved in cross-media retrieval area, in other words, the semantic relationship between potential features should be mined, which will improve their similarity. To solve the above problems, a deep correlation mining method is proposed, which trains different media features by deep learning, and then fuses the correlation between the trained features to solve the heterogeneity between different features, which will make the features of different multimedia data comparable. On this basis, Levenberg-Marquart method is applied to solve the problem that deep learning is easy to fall into local minimum solution in gradient training. Experiments on different databases show that the proposed method is effective in the field of cross-media retrieval. Compared with other advanced multimedia retrieval methods, the proposed method has achieved better retrieval results.