Image labeling method and device, readable medium and electronic equipment

Abstract

The invention relates to an image labeling method and device, a readable medium and electronic equipment, and relates to the technical field of image processing, the method comprises the following steps: inputting a to-be-labeled target image into a pre-trained image multi-classification model, obtaining a matching degree between a target image output by the image multi-classification model and each scene label in the plurality of scene labels, and obtaining a first preset number of feature maps extracted by the image multi-classification model, determining a second preset number of target scene tags in the plurality of scene tags according to the matching degree of each scene tag, and labeling the target image according to the first preset number of feature maps and the image binary classification model corresponding to each target scene tag. According to the invention, the target scene label is screened out by using the image multi-classification model, and the target image is labeled according to the image binary classification model corresponding to the target scene label, so that a plurality of scene labels can be labeled for the image, and the image labeling accuracy and calculation efficiency are improved.

Description

technical field [0001] The present disclosure relates to the technical field of image processing, and in particular, to an image labeling method, device, readable medium and electronic equipment. Background technique [0002] With the development of terminal technology and image processing technology, image processing operations that can be provided on terminal devices are becoming more and more abundant. For example, the terminal device can recognize different scenes included in the picture (for example: indoor, landscape, person, mountain, lake, beach, etc.), and perform corresponding operations according to different scenes. For example, label images according to different scenes. Normally, only one scene label can be attached to a picture. However, the information contained in a picture is diverse. For example, mountains, lakes, and people may appear in a picture at the same time. If only one scene label of "people" is assigned to the picture, the result will be inaccu...

AI Technical Summary

Problems solved by technology

However, the information contained in a picture is diverse. For example, mountains, lakes, and people may appear in a picture at the same time. If only one scene label of "people" is assigned to the picture, the result will be inaccurate and cannot meet the needs of users.

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