A cross-modal hash retrieval method and system that fuses supervisory information

A cross-modal and hashing technology, which is applied in the field of cross-modal hash retrieval methods and systems that integrate supervision information, can solve the problem of not being able to fully mine complex relationships between multi-modal data, and maintain similarity and semantics. Consistency, the effect of reducing quantization errors
CN109299216BInactive Publication Date: 2019-07-23SHANDONG NORMAL UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
SHANDONG NORMAL UNIV
Publication Date
2019-07-23
Estimated Expiration
Not applicable · inactive patent

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Abstract

The invention discloses a cross-modal hash retrieval method and system for fusing supervisory information. The method includes: constructing an image network, a text network and a fusion network; acquiring image and text feature training sample pairs, and inputting the image network and text respectively network; using the output features of the image network and the text network as the input of the fusion network, and defining the output of the fusion network; constructing an objective function of learning a unified hash code according to the output of the fusion network and the similarity between pairs; Solving the objective function to obtain a unified hash code; using the unified hash code as supervisory information, combined with semantic information, to train a hash network of a specific modality. The present invention simultaneously learns feature representation and hash coding based on an end-to-end deep learning framework, can more effectively capture the correlation between different modal data, and contribute to the improvement of cross-modal retrieval accuracy.
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Description

technical field

[0001] The present disclosure relates to a cross-modal retrieval method, and more specifically, to a cross-modal hash retrieval method and system that fuses supervisory information. Background technique

[0002] In recent years, with the dramatic growth of different types of data on the web, Approximate Nearest Neighbor (ANN) search plays an increasingly important role in related applications. For example, information retrieval, data mining, computer vision, etc. Hashing technology has become one of the most popular techniques in ANN search due to its low computational cost and high storage efficiency. The basic idea of ​​hashing is to map high-dimensional data into a compact binary coded Hamming space by learning a hash function, while preserving the similarity structure of the original space as much as possible. At present, many hashing methods applied to unimodal scenarios have been proposed. However, in the real world, data with the same semantics often...

Claims

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