Neural network image classification and recognition method based on optimized KPCA algorithm

Abstract

The invention discloses a neural network image classification and recognition method based on an optimized KPCA algorithm, and the method comprises the steps of calculating the cosine similarity of different vectors in a high-dimensional space, carrying out the dimension reduction of an original matrix of the KPCA algorithm through matrix rank minimization, reserving the effective information of original data to the maximum degree, and extracting better feature vectors to serve as weight values of convolutional layers, therefore, the problems that when an original KPCA algorithm is used for convolutional neural network image classification prediction, convolution kernel initialization calculation is complex, dimensionality disasters are likely to be caused, reliable features cannot be extracted, a whole network is difficult to train, and a network architecture is sensitive to image noise are solved. Therefore, the robustness and prediction performance of the whole network model are improved, and the effect of image classification and recognition is finally improved.

Description

technical field [0001] The invention relates to the technical field of machine vision, in particular to a neural network image classification and recognition method based on an optimized KPCA (Kernel Principal Component Analysis) algorithm. Background technique [0002] In recent years, with the development of artificial intelligence upsurge, deep learning technology has been widely used in speech recognition, natural language processing, computer vision and other fields, and has achieved great success. Image classification and recognition as a part of computer vision technology , its importance is self-evident. From the popular face recognition technology in recent years to animal and plant images, to microbial images, deep learning is constantly expanding and breaking through in the field of image classification and recognition. [0003] For image classification and recognition technology, the most widely used is convolutional neural network. Convolutional Neural Networks...

AI Technical Summary

Problems solved by technology

[0006] The present invention aims to solve the problem of unsatisfactory classification and recognition results caused by using the existing KPCA algorithm to perform convolution kernel initialization operations when the convolutional neural network is used for image classification and recognition, and provides a neural network image based on an optimized KPCA algorithm. classification identification method

Images