Picture labeling method and device, computer equipment and storage medium

Abstract

The embodiment of the invention discloses a picture labeling method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining a training sample picture, and adjusting the size of the training sample picture to a preset size threshold value; for every two training sample pictures, applying a preset feature point extraction algorithm to extract feature pointsof the training sample pictures and carrying out feature point pairing to determine feature point pairs; filtering the feature point pair by applying a preset noise filtering algorithm to obtain a first target feature point pair; if the number of the first target feature point pairs is greater than a preset feature point pair number threshold, selecting feature point pairs with the preset featurepoint pair number threshold as second target feature point pairs; calculating the distance between each feature point pair in the second target feature point pair and the sum of the distances; and ifthe sum of the distances is smaller than a preset distance threshold, marking the two training sample pictures to be similar. Labor cost of marking the training sample pictures in the picture recognition model training process based on artificial intelligence is reduced.

Description

technical field [0001] The embodiment of the present application relates to machine learning technology, and in particular to a picture labeling method, device, computer equipment and storage medium. Background technique [0002] With the continuous development of artificial intelligence technology, the application of deep learning is becoming more and more extensive. In the field of machine vision, especially in the application scenario of image search, a large amount of labeled data is required in the training process of the artificial intelligence-based image recognition algorithm model, for example, a large amount of labeled data of similar images with millions of levels or more is required , and labeling data of more than one million levels requires a lot of labor costs. The more similar pictures, the more labeled data, the more accurate the trained picture recognition algorithm model will be, and the accuracy of image search will be higher and higher. [0003] Howeve...

AI Technical Summary

Problems solved by technology

[0004] This application provides a picture labeling method, device, computer equipment and storage medium to solve the problems caused by manual labeling caused by the labeling data in the training samples of the picture recognition model based on artificial intelligence when searching for pictures by pictures in the prior art. The problem of high labor costs

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