Target image quick retrieval method and system based on artificial intelligence

A target image and artificial intelligence technology, applied in still image data retrieval, neural learning methods, still image data query, etc., can solve poor generalization performance, cannot distinguish confusing features, and does not consider mining image similarity relations And other issues

Active Publication Date: 2021-11-26
SHANDONG JIANZHU UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Therefore, popular item retrieval methods treat all samples equally, resulting in relatively poor generalization performance of item retrieval methods in complex scenarios.
[0005] (1) Item retrieval in complex scenarios contains a large number of confusing entities. These entities generally have similar feature representations, and popular item retrieval methods cannot distinguish them (without considering confusing features);
[0006] (2) Item retr

Method used

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  • Target image quick retrieval method and system based on artificial intelligence
  • Target image quick retrieval method and system based on artificial intelligence
  • Target image quick retrieval method and system based on artificial intelligence

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Embodiment 1

[0029] This embodiment provides an artificial intelligence-based method for fast retrieval of target images;

[0030] Such as figure 1 As shown, the fast retrieval method of target image based on artificial intelligence includes:

[0031] S101: Obtain a template image and several known tags corresponding to the template image;

[0032] S102: extract the image to be detected from the target image database;

[0033] S103: Input the image to be detected and the template image into the trained convolutional neural network respectively, and output the hash code of the image to be detected and the hash code of the template image;

[0034] S104: Based on the Hamming distance between the hash code of the image to be detected and the hash code of the template image, the similarity between the image to be detected and the template image is obtained. The smaller the Hamming distance, the higher the similarity, and the higher the similarity is selected. at the set threshold (set the thres

Embodiment 2

[0091] This embodiment provides an artificial intelligence-based target image fast retrieval system;

[0092] A fast retrieval system for target images based on artificial intelligence, including:

[0093] An acquisition module configured to: acquire a template image and several known labels corresponding to the template image;

[0094] An extraction module configured to: extract an image to be detected from a target image database;

[0095] A conversion module configured to: input the image to be detected and the template image into the trained convolutional neural network, and output the hash code of the image to be detected and the hash code of the template image;

[0096] An output module configured to: obtain the similarity between the image to be detected and the template image based on the Hamming distance between the hash code of the image to be detected and the hash code of the template image, the smaller the Hamming distance, the greater the similarity High, select one

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Abstract

The invention discloses a target image quick retrieval method and system based on artificial intelligence. The method comprises the following steps of acquiring a template image and a plurality of known tags corresponding to the template image; extracting a to-be-detected image from the target image database; inputting the to-be-detected image and the template image into a trained convolutional neural network, and outputting a hash code of the to-be-detected image and a hash code of the template image; based on the Hamming distance between the Hash code of the to-be-detected image and the Hash code of the template image, obtaining the similarity between the to-be-detected image and the template image, and selecting one or more to-be-detected images with the similarity higher than a set threshold value as the retrieval results to be output. By using an artificial intelligence technology, based on the convolutional neural network, image features are extracted from image samples, collected by a robot vision platform, in a complex scene by using a Hash method, by introducing the distinguishing easy-to-confusion entities, a similarity relationship can be optimized, the sample attention is distinguished, and article retrieval in the complex scene can be better coped with.

Description

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Claims

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Application Information

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Owner SHANDONG JIANZHU UNIV
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