Image classification method and apparatus based on multi-core learning classifier fusion

Abstract

The invention provides an image classification method and apparatus based on multi-core learning classifier fusion. The method comprises steps of: S1, establishing a sample library which includes different types of samples; S2, performing feature extraction on the different types of samples and obtaining kernel functions corresponding to the different types of samples according to feature extraction results; S3, synthesizing the obtained kernel functions to establish a multi-core model; S4, training the multi-core model to obtain a plurality of classifiers; S5, using an Adaboost algorithm to assign different weights to the plurality of classifiers obtained in the S4 so as to fuse the plurality of classifiers to obtain a target classifier; and S6 classifying the images to be classified by using the target classifier to obtain a classification result. The image classification method of the present invention can improve the accuracy of image classification.

Description

technical field [0001] The invention relates to the technical field of artificial intelligence, in particular to an image classification method and device based on fusion of multi-core learning classifiers. Background technique [0002] Image processing plays an increasingly important role in people's daily life, and image classification has a very important application in image processing. Including terrain detection, face recognition, tumor diagnosis and image retrieval in the Internet field, etc. [0003] Support Vector Machine (SVM) is a general learning algorithm based on Structure Risk Minimization (SRM). Its basic idea is to construct an optimal hyperplane in the sample input space or feature space. , so that the distance between the hyperplane and the two types of sample sets is maximized, so as to achieve the best generalization ability. His solution is globally optimal and does not require manual design of the network structure. For nonlinear problems, SVM tries...

AI Technical Summary

Problems solved by technology

As far as image classification is concerned, color-related features, texture-related features, and space-related features may be used. The best kernel functions corresponding to these types of features may not be the same. If they share the same kernel function, it may not be possible to get the best results. Excellent mapping, which means that more accurate classification results cannot be obtained

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