Multi-label image deep learning classification method and equipment

A technology of image depth and classification methods, applied in the field of machine learning, can solve problems such as not considering label similarity, lack of effective methods for integration, and no label label relationship constraints
CN112308115AActive Publication Date: 2021-02-02ANHUI UNIVERSITY OF TECHNOLOGY
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
ANHUI UNIVERSITY OF TECHNOLOGY
Publication Date
2021-02-02

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Abstract

The invention relates to a multi-label learning technology in the field of machine learning, and relates to a multi-label image deep learning classification method and equipment. The method comprisesthe following steps: acquiring a label relation graph; acquiring mapping of all types of labels and mapping of all label groups according to the label relation graph; constructing a deep convolutionalneural network and carrying out image general feature extraction; selecting feature maps of different layers of the convolutional neural network, and mapping the feature maps to a label and label group mapping dimension through a mapping function; calculating a conformity score and a normalization score of the label and the label group at the current pixel point position for all the pixel pointsin the selected feature maps; acquiring a final label related semantic feature and a final label group related semantic feature; and performing label prediction. According to the method, the label relationship is effectively utilized, richer image general features and label relationship features are learned, and a multi-label classification task is better carried out.
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Description

technical field

[0001] The present invention relates to multi-label learning technology in the field of machine learning, relates to graph embedding learning and classification technology in deep multi-label learning, and in particular to a multi-label image deep learning classification method and equipment. Background technique

[0002] In the era of big data, multi-label images are becoming more and more complex. The complexity of multi-label images is not only reflected in the increase in the number of labels in the image, but also in the increasingly complex distribution of different labels in multi-label images. In order to solve the classification problem of multi-label images, in addition to using the features of the image itself such as outline, shape, color, etc. for label classification, it is also possible to model the label relationship by combining the interrelationships between labels in multi-label learning.

[0003] The current deep learning of multi-label im...

Claims

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