Data retrieval method and system based on multi-graph weighted fusion

Abstract

The invention belongs to the technical field of information retrieval, and provides a data retrieval method and system based on multi-graph weighted fusion. In order to solve the problem of inaccurate retrieval, the retrieval method comprises the following steps: obtaining a mapping matrix based on a target function, projecting test data according to the mapping matrix, and correspondingly generating a test data hash code matrix; calculating a Hamming distance between the test data hash code matrix and a training data hash code matrix obtained based on the target function, and sorting the Hamming distance to obtain a retrieval result of the test data; wherein the objective function is composed of six items, and the first two items are potential factor matrixes of different modal data obtained through collaborative matrix decomposition; the third item is to learn similar graph matrixes in the modals and between the modals; the fourth item is to generate a uniform Hash code matrix through a uniform consensus graph matrix and a potential factor matrix among modals; the fifth item is a learning hash function; and the sixth item is a regularization item. The method has the characteristics of high training and retrieval speed and high retrieval performance.

Description

technical field [0001] The invention belongs to the technical field of information retrieval, and in particular relates to a data retrieval method and system based on multi-image weighted fusion. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] With the rapid development of network technology and the exponential growth of different modal data in social media, it is no longer limited to similarity retrieval between single-modal data, but more mutual retrieval between multi-modal data. The task of multimodal retrieval is to find a set of semantically similar objects in another modality given a query object in one modality, such as text retrieval image, image retrieval text, etc. However, in practical applications, data of different modalities often have inconsistent underlying feature structures, that is, the heterogeneity problem among mult...

AI Technical Summary

Problems solved by technology

However, in practical applications, data of different modalities often have inconsistent underlying feature structures, that is, the heterogeneity problem among multimodal data

At the same time, for large-scale databases, traditional multimedia retrieval technologies often cannot achieve accurate retrieval due to limitations in storage space and computing costs.

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