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Website error-reporting screenshot classification method based on feature fusion

A technology of feature fusion and classification methods, which is applied to biological neural network models, instruments, character and pattern recognition, etc., to reduce workload, improve classification accuracy, and improve classification accuracy

Inactive Publication Date: 2019-11-05
浙江网新数字技术有限公司
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  • Application Information

AI Technical Summary

Problems solved by technology

In actual network operations, due to operational errors or unsatisfied required conditions, the website will feed back error information to users, and users may directly send screenshots of error reports to website customer service personnel for consultation. When the website has a large number of visits, It requires more manual customer service to handle, which means more investment for enterprises

Method used

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  • Website error-reporting screenshot classification method based on feature fusion
  • Website error-reporting screenshot classification method based on feature fusion
  • Website error-reporting screenshot classification method based on feature fusion

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Experimental program
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Embodiment

[0043] Embodiment: As shown in the accompanying drawings, this feature fusion-based website error screenshot classification method mainly includes the following steps:

[0044] 1) Firstly, data enhancement is performed on the image data set of the screenshot of the error report to expand the data set; data enhancement mainly includes random rotation, cropping, and brightness transformation. The image rotation formula is:

[0045]

[0046] Among them, x, y represent the coordinates of the pixels in the original image, x', y' represent the coordinates of the rotated pixels, and θ represents the angle of rotation.

[0047] 2) Scale the image data to a uniform (M, M) size, and randomly divide it into a training set, a verification set, and a test set according to the ratio of a:b:c;

[0048] 3) Use part of the network layer of the VGG16 convolutional neural network to extract features from the image;

[0049] 4) Using the Scale Invariant Feature Transform (SIFT) operator to ex...

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Abstract

The invention discloses a website error-reporting screenshot classification method based on feature fusion. The method comprises the following steps: firstly, carrying out data enhancement on an imagedata set of error-reporting screenshots; zooming the image data to a uniform size, and randomly dividing the image data into a training set, a verification set and a test set; performing feature extraction on the image by using a part of network layer of the VGG16 convolutional neural network; extracting features of the image by using a scale-invariant feature transformation operator; fusing thetwo features through feature splicing to serve as final features of the image; and enabling the final features of the image to pass through a full connection layer, a Dropout layer and a Softmax layerto realize correct classification of error-reporting screenshots. According to the invention, machine learning is used to train the neural network for image classification, the workload of customer service staff is reduced, and the enterprise operation efficiency is improved; the data set is expanded by performing data enhancement on the data set image, so that the training is more sufficient; and the two image features are fused to obtain better classification accuracy.

Description

technical field [0001] The invention relates to the fields of machine learning and image classification, in particular to a method for classifying screenshots of website error reports based on feature fusion. Background technique [0002] In recent years, with the continuous development of Internet technology and the explosive growth of smart devices, people's daily life has become more and more closely integrated with the Internet. In actual network operations, due to operational errors or unsatisfied required conditions, the website will feed back error information to users, and users may directly send screenshots of error reports to website customer service personnel for consultation. When the website has a large number of visits, It requires more manual customer service to handle, which means more investment for enterprises. At the same time, with the rise of computer vision technology and machine learning algorithms, it has become a trend to combine machine learning te...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06K9/46G06N3/04
CPCG06V10/462G06N3/045G06F18/253G06F18/24
Inventor 沈越张丽
Owner 浙江网新数字技术有限公司
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