Method and device for classifying images on basis of convolutional neural network

Abstract

The invention discloses a method and a device for classifying images on the basis of a convolutional neural network. The method includes receiving various categories of inputted image samples and computing a neural network weight corresponding to each category of images; distributing the neural network weights corresponding to the various categories of images by the aid of a layered structure and forming a corresponding learning library in each layer; processing data of an inputted test category of image samples to obtain corresponding one-dimensional feature description, and performing feed-forward learning on the one-dimensional feature description corresponding to the data of the test category of image samples and the neural network weights in the learning libraries so as to judge whether the test category is available in the learned categories of images or not. The method and the device have the advantages that the problem of limitation of the traditional convolutional neural network on classification numbers can be solved by the layered distribution structure, the problem of excessive learning of the convolutional neural network can be solved, the classification capacity of the convolutional neural network can be expanded, the classification accuracy can be improved, and an image classification algorithm in new environments is high in robustness.

Description

technical field [0001] The present invention relates to the technical field of image classification methods, in particular to an image classification method and device based on a convolutional neural network. Background technique [0002] Existing image classification methods, commonly used classification methods include supervised learning methods such as neural networks and support vector machines, and unsupervised learning methods such as K-means clustering and nearest neighbor methods. The traditional neural network belongs to the supervised learning method, which obtains the neural network weight characteristic description of the learned object on the basis of learning the existing samples, and distinguishes the categories of the learned library in the external environment according to the learned knowledge. However, due to the limited knowledge of the characteristics of the objects learned, the test objects in the external changing environment may exceed the scope of t...

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is to provide an image classification method and device based on a convolutional neural network in view of the above-mentioned defects of the prior art, aiming at solving the problem that the existing neural network image classification method has poor classification effect and is prone to The problem of overlearning

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