Image processing method and device, electronic equipment and storage medium

Abstract

The invention relates to an image processing method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the feature extraction of a plurality of first images obtained in a current clustering period, and obtaining the first features of the first images; performing clustering processing on the first features, so that the category of each first image and the first clustering center of each category can be obtained; respectively determining a first feature similarity between each first clustering center and a second clustering center of each category in the reference feature library; and adding the first feature of the category to which the first clustering center of which the first feature similarity meets the threshold condition belongs tothe reference feature library. According to the image processing method provided by the embodiment of the invention, the first features meeting the threshold condition can be added to the reference feature library, and all the images do not need to be loaded into the memory for clustering processing, so that the memory can perform clustering processing with a relatively small data scale, the typesof all the first images can be automatically distinguished, and the annotation workload is reduced.

Description

technical field [0001] The present disclosure relates to the technical field of computer vision, and in particular to an image processing method and device, electronic equipment, and a storage medium. Background technique [0002] Technology based on deep learning has been widely used in various aspects such as security monitoring, intelligent customer service, and driverless driving. Existing deep learning requires a large amount of labeled data for training in order to achieve better performance. However, labeling data, that is, manually distinguishing the categories of images, requires a lot of manpower and material resources. At the same time, for millions of data, relying on manual labeling becomes very time-consuming and unrealistic. In related technologies, the category of samples can be determined by clustering, but for large-scale data, it is impossible to load all the data into the memory, so the existing clustering methods cannot be directly applied to the actual ...

AI Technical Summary

Problems solved by technology

However, labeling data, that is, manually distinguishing the categories of images, requires a lot of manpower and material resources. At the same time, for millions of data, relying on manual labeling becomes very time-consuming and unrealistic.

In related technologies, the category of samples can be determined by clustering, but for large-scale data, it is impossible to load all the data into the memory, so the existing clustering methods cannot be directly applied to the actual Using

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