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A matrix factorization hashing method based on reconstruction constraints

A matrix decomposition and matrix reconstruction technology, applied in the direction of complex mathematical operations, can solve problems affecting the semantic similarity comparison of multi-modal data, and achieve the effect of enhancing acquisition ability, reducing loss, and reducing information loss

Active Publication Date: 2019-01-22
GUANGDONG UNIV OF TECH
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Problems solved by technology

However, data in the real world often have a lot of redundant information, which will greatly affect the semantic similarity comparison of multimodal data, and the existing matrix factorization hashing method cannot handle this problem well.

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  • A matrix factorization hashing method based on reconstruction constraints
  • A matrix factorization hashing method based on reconstruction constraints
  • A matrix factorization hashing method based on reconstruction constraints

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[0036] like figure 1 Shown is the first embodiment of the reconstruction constraint-based matrix factorization hashing method, which includes the following steps:

[0037] S1. Learn the common latent semantic space matrix S of the picture and text data through matrix factorization, and obtain the mapping matrix for the query item by performing norm operation on the common semantic space matrix S, the picture matrix X and the text matrix Y P 1 and P 2 ;

[0038] In order to measure the semantic similarity between pictures and texts, it is first necessary to learn their common latent semantic space, in which data of two different modalities can measure the semantic similarity between them. This method learns the common latent semantic space S between image X and text Y by using matrix factorization, and the formula is as follows:

[0039]

[0040] In formula (1), mf represents matrix factorization, Represents the F-norm of the matrix, α is the balance parameter;

[004...

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Abstract

The invention relates to the technical field of an image processing method, more particularly, to a matrix factorization hashing method based on a reconstruction constraint. Firstly a matrix factorization technique is used to learn common latent semantic information of picture data and text data. Then a set of general mapping matrices is obtained by calculation. Then, the Graph Laplacian constraint is introduced into the latent semantic information by using the available data label information, so as to enhance the recognition ability of the method. Finally, the interference of redundant information is reduced by reconstructing the original picture and text data. The invention separates the effective information and the redundant information in the original data through the reconstructionof the original data, enhances the obtaining ability of the latent semantic information of the matrix factorization technology, thereby improving the success rate of the retrieval, and can be well applied to the large-scale cross-modal retrieval task.

Description

technical field [0001] The present invention relates to the technical field of image processing methods, and more particularly, to a matrix factorization hashing method based on reconstruction constraints. Background technique [0002] With the rapid development of the Internet, the data representation in the Internet has become diversified. For example, a web page usually contains multimedia data such as pictures, texts, videos, and audios. At present, most of the traditional retrieval methods are based on single-modality, that is, only the same type of data is retrieved, such as text retrieval, image retrieval, video retrieval and so on. How users can efficiently retrieve the data they want from multimodal data becomes a challenging problem. To solve this problem, research hotspots in the retrieval field gradually tend to cross-modal retrieval. Cross-modal retrieval can submit content in any media form to search for relevant information, and its main problem is how to me...

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Application Information

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IPC IPC(8): G06F17/16
CPCG06F17/16
Inventor 陈辉王海涛武继刚孟敏
Owner GUANGDONG UNIV OF TECH
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