Semi-automatic labeling method, equipment, medium and device

Abstract

The invention discloses a semi-automatic labeling method, which belongs to the technical field of image labeling, and comprises the following steps of: initializing each pixel generation region of a picture as a candidate region, and pairing all adjacent candidate regions in pairs to generate a calculation list; similarity calculation is carried out according to the calculation list, including color similarity calculation and texture value similarity calculation, and a first score and a second score are obtained respectively; score calculation is carried out according to the calculation list to obtain a third score; and according to the first score, the second score and the third score, adding the calculated similarities, and combining every two similarities reaching a similarity combination threshold in the list until all candidate regions are combined. After the label candidate boxes are generated by adopting the semi-automatic labeling method, only the proper candidate boxes need tobe manually selected, so that the labeling time is greatly saved, and the labeling efficiency is improved. The invention further discloses a semi-automatic labeling device, electronic equipment and acomputer storage medium.

Description

technical field

[0001] The invention relates to the technical field of image labeling, in particular to a semi-automatic labeling method, equipment, medium and device. Background technique

[0002] With the vigorous development of machine learning, the field of artificial intelligence has become increasingly inseparable from machine learning. However, machine learning requires a large amount of training data, and the training data needs to be manually labeled. However, the training data is usually tens of thousands or even hundreds of pieces. Ten thousand. However, pure manual labeling requires manual strokes, and the workload is very heavy.

[0003] And manual labeling will cause large errors, which will cause unnecessary impact on the machine learning model. However, the existing algorithm itself is specialized and only targets a specific object detection, so it cannot be used as a general object labeling aid. Contents of the invention

[0004] In order to overcome th...

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