Cross-modal hash retrieval method based on triple deep networks

Abstract

The invention provides a cross-modal hash retrieval method based on triple deep networks. The method is used to solve the technical problem of low retrieval precision existing in existing cross-modalhash retrieval methods, and includes the realization steps of: preprocessing data, and dividing the data into training data and query data; acquiring hash codes of image training data and text training data; using triple supervisory information to establish an objective loss function; carrying out orderly iterative optimization on the objective loss function; calculating hash codes of image querydata and text query data; and acquiring retrieval results of the query data. According to the solution provided by the invention, the triple information is used to construct the objective loss function, semantic information is increased, an intra-modal loss function is added at the same time, discriminability of the method is improved, and precision of cross-modal retrieval can be effectively improved. The method can be used for Internet-of-things information retrieval and image and text mutual-searching services of e-commerce, mobile equipment and the like.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and relates to mutual retrieval between large-scale image data and text data, specifically a cross-modal hash retrieval method based on a triple deep network, which can be used for Internet of Things information retrieval, electronic Image and text mutual search service for business and mobile devices. Background technique [0002] With the rapid development of Internet technology and social networking sites, massive amounts of multimedia data, such as text, images, video, and audio, are generated every day. The mutual retrieval of cross-modal data has become a research hotspot in the field of information retrieval. Hash method is a very effective information retrieval method, which has the advantages of low memory consumption and fast retrieval. Hash methods can be divided into single-modal hash methods, multi-modal hash methods and cross-modal hash methods. The query data and retrieva...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to address the above-mentioned deficiencies in the prior art, and propose a cross-modal hash retrieval method based on a triple deep network, which is used to solve the technical problem of low retrieval accuracy existing in the existing cross-modal hash retrieval method

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