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Semi-automatic labeling method for image data

An image data, semi-automatic technology, applied in the computer field, can solve the problems of insufficient model accuracy and poor effect, and achieve the effect of ensuring accuracy

Active Publication Date: 2021-01-26
TIANJIN UNIV
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Problems solved by technology

[0005] However, there are certain shortcomings in the above two methods. The model obtained by the first method mentioned in the previous paragraph is often not accurate enough. In practical applications, the effect is often very poor.

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  • Semi-automatic labeling method for image data
  • Semi-automatic labeling method for image data
  • Semi-automatic labeling method for image data

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Embodiment Construction

[0021] The following examples can enable those skilled in the art to understand the present invention more fully, but do not limit the present invention in any way. The experimental methods in the following examples are conventional methods unless otherwise specified.

[0022] Before explaining the present invention, two concepts involved in the present invention are first clarified: Image-level labeling refers to labeling only its category for an image, and not labeling specific locations in each object. Instance-level annotation refers to annotating the category of an image for an image, and also specifying the specific location boxes of each target in the image.

[0023] The method of the present invention mainly includes the following steps: 1. Carry out image-level labeling to a small amount of unlabeled images; 2. Put the labeled data in figure 1 Training in the model in , this model is called the collaborative weak supervision recognition model; 3. After the training i...

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Abstract

The invention provides a semi-automatic labeling method for image data, which includes: performing partial image-level labeling on unlabeled images; putting the labeled data into a collaborative weakly supervised recognition model for training; transforming the collaborative weakly supervised recognition model to obtain strong Supervised transformation model; use the strong supervision transformation model to detect unlabeled images and obtain the detection results; use artificially labeled image data to train the strong supervision transformation model. The method of the present invention uses a certain amount of weakly labeled data in the early stage, and then uses active learning to gradually improve the model in the later stage, thereby ensuring accuracy while reducing the amount of labeling.

Description

technical field [0001] The invention relates to the field of computers, and more specifically, to a semi-automatic labeling method for image data. Background technique [0002] With the development of artificial intelligence technology, a large number of machine learning models require a large amount of manually labeled data. However, this poses great challenges to the practical application of artificial intelligence technology. When a mature model is formed, a lot of manpower is needed to label the data, and the accuracy and accuracy of the data labeling directly affect the quality of the training model. In the field of computer vision, the accuracy rate of the image target recognition model based on strong supervision is already very high. However, this is increasingly dependent on manually labeled data. Generally, it takes a lot of manpower, material resources and resource costs to obtain such a large amount of strongly supervised labeling data. [0003] However, image...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06N20/00
CPCG06N20/00G06F18/241G06F18/214
Inventor 胡清华杨家安谢宗霞
Owner TIANJIN UNIV
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