Cross-modal hash retrieval method based on self-learning

Abstract

A cross-modal hash retrieval method based on self-learning belongs to the technical field of computers, and comprises the following steps: 1) learning potential public semantic features of different modals through a co-matrix decomposition technology; 2) learning a unified binary code with discrimination through orthogonal transformation and quantization processes; 3) maintaining and integrating the similarity in the modes and the similarity between the modes into a graph regularization item, and embedding the graph regularization item into a binary code generation process; 4) calculating andoptimizing a target function, and iteratively updating a plurality of matrix variables until a convergence condition is met; and 5) completing learning of a specific modal hash function by adopting aself-learning framework. Aiming at the problem of large quantization error caused by a threshold strategy, the binary coding loss of common representation of different modes is minimized, the similarity between the interiors of the modes and the similarity between the modes are embedded, and a self-learning hash scheme is introduced to learn a hash function with higher discrimination. Coding errors in a binary quantization stage can be effectively reduced, and the quality of hash codes and the performance of cross-modal retrieval are improved.

Description

technical field [0001] The invention belongs to the technical field of computers and relates to a cross-modal hash retrieval method based on self-learning. Background technique [0002] With the rapid development of information retrieval technology and the popularization of various digital devices, a large amount of multimedia data, such as text, images, and videos, appears in the Internet. These multimedia data not only have a considerable amount, but also contain a variety of modalities of different dimensions. Since data of different modalities usually describe the same object or event, how to use one of the modal data to retrieve the results of other modalities related to it has become an urgent problem to be solved. In recent years, many researchers have invested in the field of cross-modal retrieval and achieved great success. However, when the data dimension is high and the scale is large, the retrieval cost of most cross-modal retrieval methods can be very large. ...

AI Technical Summary

Problems solved by technology

[0004] However, both unsupervised hashing methods and supervised hashing methods have a common limitation: in the quantization stage, most of them ignore the binary constraints and adopt a simple threshold strategy to generate the final binary hash code, which will lead to a large number of quantization errors and reduce the discrimination ability of binary codes

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