Cross-modal hash retrieval method based on triple deep networks

A deep network and triplet technology, applied in the field of computer vision, can solve the problem of low retrieval accuracy, and achieve the effect of improving accuracy, enriching semantic information, and increasing discriminativeness.

Active Publication Date: 2018-06-15
XIDIAN UNIV
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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 ba

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  • Cross-modal hash retrieval method based on triple deep networks
  • Cross-modal hash retrieval method based on triple deep networks
  • Cross-modal hash retrieval method based on triple deep networks

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[0036] Below in conjunction with the accompanying drawings and specific embodiments, the present invention will be described in further detail,

[0037] refer to figure 1 , the present invention comprises the steps:

[0038] Step 1) Preprocess the data:

[0039] Determine the data of two modalities: image data and text data, use the word2vec method to extract the Bag-of-words feature of the text data to represent the text in a vector form for computer processing, and extract the original pixel features of the image data to retain the original information of the image; Take 80% of the image data as image training data, and the rest as image query data; take the text data corresponding to the image training data as text training data, and the rest as text query data;

[0040] Step 2) Obtain the hash codes of image training data and text training data:

[0041] Input the Bag-of-words features of the text training data into the text deep network to obtain the text training data...

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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...

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Application Information

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IPC IPC(8): G06F17/30G06K9/62G06N3/08
CPCG06F16/35G06F16/583G06N3/084G06F18/22
Inventor 邓成陈兆佳李超杨二昆杨延华
Owner XIDIAN UNIV
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