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Image multi-label classification method, system and device and readable storage medium

A classification method and multi-label technology, which is applied in still image data clustering/classification, neural learning methods, still image data retrieval, etc., can solve the problems of insufficient precision and low accuracy of image multi-label classification, and achieve improved image quality. The effect of multi-label classification accuracy, improved accuracy and accuracy, and enhanced interpretability

Active Publication Date: 2020-11-06
THE FIRST AFFILIATED HOSPITAL OF MEDICAL COLLEGE OF XIAN JIAOTONG UNIV
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AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to overcome the shortcomings of insufficient high precision and low accuracy of image multi-label classification in the above-mentioned prior art, and provide a method, system, device and readable storage medium for image multi-label classification

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  • Image multi-label classification method, system and device and readable storage medium
  • Image multi-label classification method, system and device and readable storage medium
  • Image multi-label classification method, system and device and readable storage medium

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Embodiment Construction

[0045]In order to enable those skilled in the art to better understand the solutions of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only It is an embodiment of a part of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0046] It should be noted that the terms "first" and "second" in the description and claims of the present invention and the above drawings are used to distinguish similar objects, but not necessarily used to describe a specific sequence or sequence. It is to be understood that the data so used are interchangeable under appropriate c...

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Abstract

The invention belongs to the field of image classification, and discloses an image multi-label classification method, system and device, and a readable storage medium. The method comprises the steps:S1, obtaining original images corresponding to multiple labels to be classified, and generating a sample set; s2, obtaining an initial deep convolutional neural network for image multi-label classification, and adding a gate activation function layer to obtain a deep convolutional neural network; s3, iteratively training a deep convolutional neural network through the sample set to obtain a saliency map corresponding to each label; s4, selecting a saliency map corresponding to a label with a preset requirement, and extracting a target from the corresponding original image through the saliencymap; s5, setting a to-be-generated image, and migrating the target area to the to-be-generated image to obtain a target image; s6, adding the target image into the sample set, iterating S3 to S5 for preset times, and performing image multi-label classification of the input image through the final deep convolutional neural network. According to the method, the precision and the accuracy of image multi-label classification are effectively improved.

Description

technical field [0001] The invention belongs to the field of image classification, and relates to an image multi-label classification method, system, equipment and readable storage medium. Background technique [0002] Image multi-label classification has always been a hot issue in the field of computer vision. With the rise and development of artificial intelligence technology, image multi-label classification methods based on deep learning, especially image multi-label classification using deep convolutional neural networks, have made significant progress. A breakthrough, its classification accuracy far exceeds the traditional machine learning method. However, due to the huge amount of model parameters based on deep convolutional neural networks, the results generally lack interpretability, which makes the results of image multi-label classification based on this method unreliable, which has become a bottleneck for the further development and application of deep learning. ...

Claims

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Application Information

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IPC IPC(8): G06F16/55G06N3/04G06N3/08
CPCG06F16/55G06N3/08G06N3/045
Inventor 蔺琛皓沈超朱炯历王骞李琦
Owner THE FIRST AFFILIATED HOSPITAL OF MEDICAL COLLEGE OF XIAN JIAOTONG UNIV
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