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Marking method of images, marking apparatus of images, marking equipment of images, and storage medium

A technology for storage media and pictures, applied to instruments, character and pattern recognition, computer components, etc., can solve the problems of low quality labeling of sample pictures, errors, and uneven level of labeling personnel

Active Publication Date: 2018-12-07
BEIJING KINGSOFT CLOUD NETWORK TECH CO LTD +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, mistakes are inevitable in manual labeling, and the level of labeling personnel is uneven. For the amount of data up to millions of levels, when the administrator manually screens out pictures with wrong label categories, it is easy to miss, and the quality of sample picture labeling is low.

Method used

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  • Marking method of images, marking apparatus of images, marking equipment of images, and storage medium
  • Marking method of images, marking apparatus of images, marking equipment of images, and storage medium
  • Marking method of images, marking apparatus of images, marking equipment of images, and storage medium

Examples

Experimental program
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Effect test

Embodiment 1

[0098] like figure 1 As shown, the embodiment of the present invention provides a picture labeling method, which can be specifically applied to a server, and of course can also be applied to other types of devices with data processing and computing functions. The server is used as an example for description below. Include the following steps:

[0099] S101: Acquire a plurality of pictures of to-be-labeled categories, and divide the plurality of pictures into several parts.

[0100] In this embodiment of the present invention, the server may acquire multiple pictures. Take the marked icon as a training sample for a machine learning model, which is used for category prediction of live video content. These pictures need to be screenshots of the live video in the live room. A screenshot of the live video in a live video room, or multiple screenshots taken at multiple points in the live video of a live video room. Correspondingly, since the acquired picture has not yet been mark...

Embodiment 2

[0144] like image 3 As shown, the embodiment of the present invention also provides a picture labeling method, which can be applied to the server, and of course can also be applied to other types of devices with data processing and computing functions. The process may include the following steps:

[0145] S301. Use the pictures whose labeled categories are determined as training samples of the machine learning model to obtain the machine learning model.

[0146] In the embodiment of the present invention, the pictures whose marked categories are determined can be used as training samples of the machine learning model, so as to train the machine learning model and obtain the machine learning model for predicting the picture category. Wherein, the above-mentioned picture whose category is determined to be marked may be a picture after the category is determined by the labeling method of the method embodiment 1, and of course it may also be other pictures marked with a category....

Embodiment 3

[0162] like Figure 4 As shown, an embodiment of the present invention also provides a method for acquiring a machine learning model, which can be applied to a server, and of course can also be applied to other types of devices with data processing and computing functions. In this embodiment, the process of training the machine learning model and obtaining the machine learning model, such as Figure 4 As shown, the following steps may be specifically included:

[0163] S401: Obtain a first sample picture marked with a category.

[0164] The first sample picture is a picture with a marked category, for example, a picture marked with a category by an annotator and the marked category of the picture has been determined.

[0165] Specifically, the first sample picture marked with a category can be obtained by using the marking method in Method Embodiment 1.

[0166] S402 , using the first sample picture marked with the category as a training sample, and training to obtain a mac...

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Abstract

The embodiment of the invention also provides a marking method of images, a marking apparatus of images, marking equipment of images, and a storage medium. The marking method of images includes the steps: acquiring a plurality of images to be marked for types, and dividing the images into a plurality of sections; allocating each section of divided images to at least two markers; for each section of divided images, acquiring marking result data of the at least two markers; for each image in each section of images, comparing whether the pre-marked type of the image is the same in the marking result data of the at least two markers, and determining the quantity of images with the same pre-marked type in the marking result data of the at least two markers in each section of images; and for each section of divided images, based on the proportion relation between the determined quantity of the images with the same pre-marked type and the total quantity of the section of images, determining the marked type of the images in the section of images. The marking method of images can improve the marking quality of the marking type of images.

Description

technical field [0001] The present invention relates to the technical field of machine learning, and in particular, to a picture labeling method, labeling device, equipment and storage medium. Background technique [0002] With the popularity of live video, a large amount of vulgar pornography and other bad content is produced in the live video content. Therefore, it is necessary to effectively supervise the live video content. At present, each live broadcast platform generally supervises the live broadcast content manually, and configures a supervision team with up to hundreds of people, and the team personnel inspect the live broadcast room to identify the bad live broadcast content. But this approach to regulation is costly and inefficient. With the continuous development of artificial intelligence and machine learning technology, deep learning technology can be used to realize the automatic identification of video content by machines. [0003] The deep learning of the ...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/22G06F18/241
Inventor 刘世权刘弘也苏驰
Owner BEIJING KINGSOFT CLOUD NETWORK TECH CO LTD
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