Self-adaptive picture classification method in semi-supervised field based on hierarchical relationship

A hierarchical relationship and semi-supervised technology, applied in the direction of neural learning methods, instruments, biological neural network models, etc., can solve the problem of lack of hierarchical relationship between sample categories, etc., and achieve the effect of improving classification effect and ideal effect

Active Publication Date: 2021-07-16
BEIJING INSTITUTE OF TECHNOLOGYGY
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Problems solved by technology

[0005] The purpose of the present invention is to overcome the deficiencies in the prior art, in order to solve the technical problem of the lack of sample category hierarchical relationship in the existing minimum maximum entropy semi-supervised domain adaptive method, a semi-supervised domain adaptive method based on hierarchical relationship is proposed Image Classification Method

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  • Self-adaptive picture classification method in semi-supervised field based on hierarchical relationship
  • Self-adaptive picture classification method in semi-supervised field based on hierarchical relationship
  • Self-adaptive picture classification method in semi-supervised field based on hierarchical relationship

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Embodiment

[0062] This embodiment is the overall flow and network structure of the semi-supervised domain adaptive method.

[0063] A semi-supervised domain adaptive image classification method based on hierarchical relationships, such as figure 1 shown, including the following steps:

[0064] Step 1: Preprocess training and test data. Prepare two image datasets with different fields but the same category space, that is, datasets with different conditions such as image style, illumination, resolution, etc. but contain the same category, and select a field where all images are labeled as the source domain. The domain with only a small number of labels is used as the target domain. For the target domain data, randomly select 1 or 3 labeled data in each category as the labeled target domain data Then randomly select 3 labeled data in each category in the remaining data as the validation set, and the remaining image data as the unlabeled target data For source domain data, the present ...

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Abstract

The invention relates to a self-adaptive picture classification method in a semi-supervised field based on hierarchical relationship, belongs to the technical field of computer vision processing, and can accurately classify images in a target domain. According to the method, a hierarchical relationship between categories is introduced, and hierarchical relationship information is provided for a prototype by utilizing parent class label and child class label information of all source domains and a small amount of labeled target domain data, so that the prototype distance of the same parent class in a prototype space is relatively short, and a self-adaptive model in the semi-supervised field is helped to obtain a better classification effect. According to the method, the maximum and minimum entropy adversarial learning is carried out on the model by using the gradient inversion layer and using the unsupervised data, so that the prototype vector which has resolution for categories and is not specific to a certain field is extracted, and the classification effect of the model on target domain data is improved. According to the method, the effect on a data set with large domain offset and a large number of categories is ideal, and it is indicated that the method can solve the complex domain offset problem.

Description

technical field [0001] The present invention relates to a semi-supervised field self-adaptive image classification method based on a hierarchical relationship, in particular to a semi-supervised field self-adaptive image classification method based on the hierarchical relationship between subclasses and parent classes, using confrontation ideas and various loss functions The classification method belongs to the technical field of computer vision processing. Background technique [0002] In recent years, deep learning networks have achieved great success in image classification tasks, but the training of deep networks requires a large amount of manually labeled data. However, it is very time-consuming and energy-consuming to manually label all collected data in practical application scenarios. , and even some data annotations for special problems can only be done by experts in related fields. [0003] In order to better deal with unlabeled or only a small number of labeled d...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06N3/088G06N3/048G06N3/045G06F18/2155G06F18/24
Inventor 宋丹丹刘瑞平
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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