Cross-modal Hash retrieval method based on deep learning

A deep learning, cross-modal technology, applied in still image data retrieval, unstructured text data retrieval, text database indexing, etc., can solve the problem of not being able to mine original feature identification information well

Active Publication Date: 2019-07-16
JIUJIANG UNIVERSITY
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Problems solved by technology

[0007] The purpose of the present invention is to provide a cross-modal hash retrieval method based on deep learning, which solves the problem that the...

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  • Cross-modal Hash retrieval method based on deep learning
  • Cross-modal Hash retrieval method based on deep learning
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[0034] The technical solution of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0035] The invention discloses a cross-modal hash retrieval method based on deep learning, such as figure 1 As shown, the specific implementation process mainly includes the following steps: Assume that the pixel feature vector set of the image modality of n objects is Among them, v i Represents the pixel feature vector of the i-th object in the image modality; let Represents the eigenvectors of these n objects in the text mode, where, t i Represents the feature vector of the i-th object in the text mode; expresses the category label vector of n objects as Among them, c represents the number of object categories; for the vector y i For example, if the i-th object belongs to the k-th class, let the vector y i The kth element of is 1, otherwise, the vector y i The kth element of is 0;

[0036] (1) Construction of cross-modal ...

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Abstract

The invention discloses a cross-modal Hash retrieval method based on deep learning. Assuming that the pixel feature vector set of the image modality of n objects is shown in the specification, the method is characterized by comprising the following steps: (1) obtaining binary Hash codes B shared by an image mode and a text mode, deep neural network parameters theta gamma and theta i of the image mode and the text mode, and projection matrixes Pv and Pi of the image mode and the text mode by using an objective function designed based on a deep learning technology; (2) solving unknown variablesB, theta gamma, theta i, Pv and Pi in the objective function in an alternative updating mode; (3) based on the obtained deep neural network parameters theta gamma and theta i of the image modality andthe text modality, and the projection matrixes Pv and Pi, generating the binary Hash codes; (4) calculating a Hamming distance from the query sample to each sample in the retrieval sample set based on the generated binary hash code; and (5) finishing retrieval of the query sample by using a cross-mode retriever based on approximate nearest neighbor search. According to the method, the cross-modalHash retrieval performance is effectively improved.

Description

technical field [0001] The invention relates to a cross-modal hash retrieval method based on deep learning. Background technique [0002] With the rapid development of science and technology and social productivity, the era of big data has come quietly. The so-called big data refers to the collection of data that cannot be captured, managed and processed by conventional software tools within a certain period of time. IBM proposed that big data has 5V characteristics, namely: Volume (large amount of data), Variety (variety of types and sources), Value (data value density is relatively low, but sometimes it is precious), Velocity (data growth rate is fast) ), Veracity (quality of data). Big data can also be considered as an information asset that requires a new processing model to have stronger decision-making power, insight discovery power, and process optimization capabilities. [0003] Information retrieval is an important aspect of data processing, and in the face of bi...

Claims

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Application Information

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IPC IPC(8): G06F16/31G06F16/51
CPCG06F16/325G06F16/51
Inventor 董西伟邓安远周军杨茂保孙丽胡芳贾海英王海霞
Owner JIUJIANG UNIVERSITY
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