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Active learning self-iteration image classification method and system

A technology of active learning and classification methods, applied in the field of deep learning, can solve problems such as a large number of labeling costs and poor effects

Pending Publication Date: 2021-03-26
SHANGHAI MININGLAMP ARTIFICIAL INTELLIGENCE GRP CO LTD
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AI Technical Summary

Problems solved by technology

[0003] The embodiment of the present application provides an active learning and self-iterative image classification method and system to at least solve the problem that the existing deep learning image classification method is poor in multi-category classification and requires a large amount of labeling costs

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  • Active learning self-iteration image classification method and system
  • Active learning self-iteration image classification method and system

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Embodiment Construction

[0024] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be described and illustrated below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, and are not intended to limit the present application. Based on the embodiments provided in the present application, all other embodiments obtained by persons of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0025] Obviously, the accompanying drawings in the following description are only some examples or embodiments of the present application, and those skilled in the art can also apply the present application to other similar scenarios. In addition, it can also be understood that although such development efforts may be complex and lengthy, for those of ...

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Abstract

The invention provides an active learning self-iteration image classification method and system. The technical scheme of the method comprises a model training step: carrying out the manual marking ofthe type of a target in a training sample, inputting the training sample which has been manually marked into a target detection model and a first-degree learning model, and carrying out the first-degree learning of the target; training the target detection model and the metric learning model; and an inference calculation step: using the trained target detection model and the metric learning modelto identify and classify the target in a to-be-detected image. The method solves the problems that an existing deep learning image classification method is poor in effect and needs a large amount of annotation cost during multi-class classification.

Description

technical field [0001] The invention belongs to the field of deep learning, in particular to an image classification method and system for active learning and self-iteration. Background technique [0002] The method based on deep learning classification will decrease the recall rate and precision rate when there are too many target categories. This is because too many categories will put a certain pressure on the classifier, and it is difficult for the classifier to fit a particularly large number of categories. ; At the same time, if the intra-class spacing of the data is too large and the inter-class spacing is too small, the method of multi-object detection lacks intra-class constraints, and the effect is poor; and if there are too many categories, it is more likely to have the problem of category imbalance. Affect the detection effect. At present, if you want to solve the problem of multi-target classification through deep learning classification, you need a lot of and ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/08
CPCG06N3/08G06V2201/07G06F18/22G06F18/24G06F18/214
Inventor 胡郡郡
Owner SHANGHAI MININGLAMP ARTIFICIAL INTELLIGENCE GRP CO LTD
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