Image retrieval method and system

Abstract

The invention discloses an image retrieval method. The method comprises the steps that an image input by a user is acquired, and the image is input into a trained similarity hash function network to generate a similarity hash code; the generated similarity hash code and each hash code in a hash code library of a to-be-retrieved image is subjected to bitwise exclusive OR operation to generate a plurality of Hamming distances, and multiple image samples corresponding to the Hamming distances less than or equal to a hash code with a set threshold serve as retrieval results. The image retrieval method can solve the technical problems of low retrieval accuracy, poor method generalization capability, excessive classification, poor adaptability to sample data and large time consumption of existing content-based image similarity hash.

Description

technical field [0001] The invention belongs to the technical field of image retrieval, and more particularly relates to an image retrieval method and system. Background technique [0002] With the continuous expansion of image data scale, image retrieval for large image datasets has been attracting the attention of researchers, among which content-based image similarity hashing is an effective image retrieval method in the big data environment. [0003] Currently, content-based image similarity hashing mainly includes effective feature extraction methods, data-aware hashing methods, classification label methods, and deep network methods. [0004] Effective feature extraction methods are mainly based on manually drawn features, such as color histograms, GIST, SIFT, BIG, etc., but the feature extraction model of this method is a shallow model, which cannot reveal the high-level semantic information contained in the feature data in essence. Poor performance in dealing with th...

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Problems solved by technology

[0008] Aiming at the above defects or improvement needs of the prior art, the present invention provides an image retrieval method and system, the purpose of which is to solve the problems of low retrieval accuracy and method generalization ability existing in the existing content-based image similarity hash Poor and there are technical problems such as excessive classification, poor adaptability to sample data, and large time consumption

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