Article image re-identification method based on multi-feature information capture and correlation analysis

Abstract

An article image re-identification method based on multi-feature information capture and correlation analysis uses a convolutional layer with a space attention mechanism and a channel attention mechanism to weight an input feature map, considers the effective combination of channel and space information, not only can pay attention to important features, but also can suppress unnecessary features, and the representation of the concerns can be improved, so that better features are obtained. By using a transformer, the features after image partitioning can be better processed by utilizing a multi-head attention mechanism, richer feature information can be captured, and the correlation among the features can be considered, so that good performance can be obtained, and the efficiency of object image retrieval can be improved. According to the method, the convolutional layer with the channel attention mechanism and the space attention mechanism and the transformer with the multi-head attention mechanism are combined, relatively important features can be paid attention to globally, and fine-grained features can be better captured, so that the re-recognition performance can be well improved.

Description

technical field [0001] The invention relates to the technical field of image retrieval, in particular to an item image re-identification method based on multi-feature information capture and correlation analysis. Background technique [0002] In recent years, the rapid development of artificial intelligence, computer vision and other technologies have been widely used in various fields. With the continuous development of the information age, the combination of computer vision and item sales and management has also become a hot spot. Given a queried item image, item image re-identification is able to retrieve all images of the same item from multiple different cameras. Item re-identification technology can not only improve people's shopping experience, but also save a certain amount of cost, improve productivity, and reduce the loss rate of items. The item image re-identification system is also widely used, not only in small stores, supermarkets and other retail industries,...

AI Technical Summary

Problems solved by technology

[0003] In the prior art, it can be divided into image re-identification methods based on manually designed features and image re-identification methods based on deep learning. The image re-identification method based on manually designed features uses the inherent attributes in the image to re-identify the image. However, the image categories that can be predicted are limited, the generalization ability is poor, and it is time-consuming

Some image re-recognition methods based on deep learning focus on global information, fail to capture subtle differences between features, ignore the importance of local information, and some can only capture some of the more important information, not very Taking into account the overall information well, resulting in low accuracy of image re-identification

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