EGC image classification method based on CNN + SVM

Abstract

The invention belongs to the technical field of image classification, and particularly relates to an EGC image classification method based on CNN + SVM, and the method comprises the following steps: converting all data into a grey-scale map, omitting the color information of a sample, increasing the contrast of an image, and carrying out the normalization processing of the data converted into thegrey-scale map, thereby obtaining an original data set; dividing the data set to obtain a training set and a test set; extracting data features in two modes of automatic extraction and manual extraction; and performing weighted summation on the classification results of the CNN and SVM algorithm models to obtain a final classification result. According to the method, a traditional classification method SVM and a deep learning method CNN are combined, feature extraction is carried out on the EGC data in a mode of combining manual features and automatic features, the interpretability of the model is improved through the use of the manual features, the potential safety hazard of the model is reduced, and the model is helped to have higher accuracy compared with a traditional method through the addition of the automatic features. The method is used for classifying the EGC images.

Description

technical field [0001] The invention belongs to the technical field of image classification, and in particular relates to a CNN+SVM-based EGC image classification method. Background technique [0002] The feature extraction method in the prior art is single, and cannot comprehensively extract EGC image features, and the manual feature extraction method cannot extract more hidden features, resulting in poor classification effect. The convolutional neural network is a black-box model, and its parameter composition method cannot be explained, and there are security risks. Contents of the invention [0003] Aiming at the above-mentioned technical problems of poor classification effect and hidden safety hazards of the existing feature extraction methods, the present invention provides a CNN+SVM-based EGC image classification with higher interpretability, lower safety risks, and higher accuracy. method. [0004] In order to solve the problems of the technologies described abov...

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Problems solved by technology

[0003] Aiming at the above-mentioned technical problems of poor classification effect and hidden safety hazards of the existing feature extraction methods, the present invention provides a CNN+SVM-based EGC image classification with higher interpretability, lower safety risks, and higher accuracy. method

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