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Construction method of image classification model, image classification method and storage medium

A classification model and construction method technology, applied in the field of image processing, can solve the problem of low classification accuracy, achieve high accuracy, suppress useless background information, highlight key feature information, and suppress redundant information.

Active Publication Date: 2021-07-30
HUAZHONG UNIV OF SCI & TECH
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  • Claims
  • Application Information

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Problems solved by technology

[0004] Aiming at the above defects or improvement needs of the prior art, the present invention provides a method for constructing an image classification model, an image classification method and a storage medium to solve the problem of accurate classification due to failure to make full use of feature information of different scales in the prior art. less technical issues

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  • Construction method of image classification model, image classification method and storage medium
  • Construction method of image classification model, image classification method and storage medium
  • Construction method of image classification model, image classification method and storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0035] A method for building an image classification model, comprising the following steps:

[0036] S1, building an image classification model; figure 1 As shown, the image classification model includes: sequentially cascaded convolutional layers, the first pyramidal convolutional unit, the second pyramidal convolutional unit, ..., the nth pyramidal convolutional unit, pooling layer and fully connected layer; the first A convolutional layer is used to extract the initial feature map of the input image and output it to the first pyramidal convolution unit; the i-th pyramidal convolutional unit is used to use convolution kernels of different scales with a number of n-i+1 to respectively analyze the current After further feature extraction is performed on the feature map input to the i-th pyramid convolution unit, the feature map extracted by each scale convolution kernel is sequentially fused with the fusion feature map extracted by the previous convolution kernel to obtain eac...

Embodiment 2

[0064] An image classification method, comprising: inputting an image to be classified into an image classification model constructed by using the method for constructing an image classification model provided in Embodiment 1, to obtain a classification result. Preferably, before the image to be classified is input to the image classification model, the image to be classified is scaled to improve calculation efficiency.

[0065] The relevant technical solutions are the same as those in Embodiment 1, and will not be repeated here.

Embodiment 3

[0067] A machine-readable storage medium, the machine-readable storage medium stores machine-executable instructions, and when the machine-executable instructions are called and executed by a processor, the machine-executable instructions cause the processor to implement The construction method of the image classification model provided in Example 1 and / or the image classification method provided in Embodiment 2.

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Abstract

The invention discloses a construction method of an image classification model, an image classification method and a storage medium. The constructed image classification model comprises a convolution layer, a first pyramid convolution unit, a second pyramid convolution unit... an nth pyramid convolution unit, a pooling layer and a full connection layer which are cascaded in sequence. The ith pyramid convolution unit adopts (n-i + 1) convolution kernels with different scales to perform further feature extraction on the currently input feature map, and then the feature map extracted by the convolution kernel of each scale is fused with the fusion feature map extracted by the previous-stage convolution kernel in sequence, a fusion feature map extracted by the convolution kernel of each scale is obtained, namely a group of feature maps containing different scale information; the feature map containing different scale information is fused with a currently input feature map to obtain an output feature map containing multi-scale information; i = 1, 2,..., n; according to the invention, different scale information is fully utilized, and the image classification accuracy is high.

Description

technical field [0001] The invention belongs to the technical field of image processing, and more specifically relates to a method for constructing an image classification model, an image classification method and a storage medium. Background technique [0002] Image classification technology is the core of computer vision and has a wide range of applications in many fields, such as: face recognition and intelligent video analysis in the security field, traffic scene recognition in the traffic field, image retrieval in the Internet field, and medical images in the medical field analysis etc. Taking medical images as an example, doctors can identify images collected by imaging equipment (such as nuclear magnetic resonance imaging, ultrasound imaging, and optical tomography) in clinical diagnosis to achieve disease screening purposes. However, the effect of artificial recognition greatly depends on the clinical experience of doctors. At the same time, the efficiency of doctor...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04G06T3/40
CPCG06T3/4038G06T2200/32G06N3/045G06F18/25G06F18/214
Inventor 张旭明周权
Owner HUAZHONG UNIV OF SCI & TECH
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