Image recognition method and device, electronic equipment and storage medium

Abstract

The invention discloses an image recognition method and device, electronic equipment and a storage medium. The image recognition method comprises the steps of obtaining a to-be-recognized image; inputting the to-be-recognized image into a pre-trained multi-label classification model, wherein the multi-label classification model comprises a sharing module, a main body classification module and a non-main body classification module, the sharing module is used for extracting shared image features of the image to be recognized and inputting the shared image features into the main body classification module and the non-main body classification module, the main body classification module is used for outputting a main body label corresponding to a main body object in a to-be-identified image according to shared image features, and the non-main body classification module is used for outputting a non-main body label corresponding to a scene in the to-be-recognized image according to the sharedimage features; and obtaining a main body label and a non-main body label output by the multi-label classification model, and taking the main body label and the non-main body label as image identification results of the to-be-recognized image. According to the method, the accuracy and recall rate of image recognition can be improved while multi-label recognition of the image is realized.

Description

technical field [0001] The present application relates to the field of computer technology, and more specifically, to an image recognition method, device, electronic equipment and storage medium. Background technique [0002] Image recognition is an important research branch in data mining technology, which aims to construct a classification function or classifier by training image sample data sets, and use the classification function or classifier to identify the label or label set of the image to be tested. Existing image recognition methods are often single-label, that is, each image corresponds to a unique category label. However, in practical applications, due to the complexity of objective things, an image often contains multiple categories of content, which is more in line with human cognitive habits, so an image may also contain multiple different labels at the same time. Therefore, the accuracy of current image recognition methods is not high. Contents of the inv...

AI Technical Summary

Problems solved by technology

However, in practical applications, due to the complexity of the objective things themselves, an image often contains multiple categories of content, which is more in line with people's cognitive habits, so an image may also contain multiple different labels at the same time

Therefore, the accuracy of current image recognition methods is not high

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