Unsupervised image classification method based on automatic encoder

Abstract

The invention discloses an unsupervised image classification method based on an automatic encoder. The method comprises the following steps: S1, designing an automatic encoder model based on a convolutional neural network architecture; S2, enabling an auto-encoder model to extract feature information in the image through multi-task loss; S3, preparing to-be-classified image data, and carrying out the self-encoder model training; S4, after the auto-encoder model is trained, enabling the auto-encoder model to complete the encoding of feature information in the image; S5, filtering the noise and background information in the original image through coding of the auto-encoder model, and completing the image classification. According to the method, learning can be carried out without any label or model, so that the image classification task is completed, and the labor cost is reduced.

Description

technical field [0001] The invention relates to an image classification method, in particular to an unsupervised image classification method based on an automatic encoder. Background technique [0002] With the development of information technology, image data has increased dramatically, and the demand for image processing has also greatly increased. In real life, due to factors such as blurred images, unclear fonts, and shooting angles, the quality of collected images is often not high. affect the accuracy of image classification. Traditional image classification methods require manual design of features, which is cumbersome and has low accuracy. Although the current deep learning-related methods do not require manual feature selection and have high accuracy, they often require a large amount of labeled data, and the cost of manual labeling is high. Contents of the invention [0003] The object of the present invention is to provide an unsupervised image classification ...

AI Technical Summary

Problems solved by technology

[0002] With the development of information technology, image data has increased dramatically, and the demand for image processing has also greatly increased. In real life, due to factors such as blurred images, unclear fonts, and shooting angles, the quality of collected images is often not high. Affected the accuracy of image classification

Traditional image classification methods require manual design of features, which is cumbersome and has low accuracy

Although the current deep learning related methods do not require manual feature selection and have high accuracy, they often require a large amount of label data, and the cost of manual labeling is high.

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