Image recognition using convolutional neural network combined with ensemble learning algorithm

Abstract

An image recognition algorithm based on ensemble learning algorithm and convolution neural network structure (ELA-CNN) is proposed to solve the problem that a single convolution neural network (CNN) classifier may be more prone to error or unreliable prediction. In order to improve the effect of ensemble learning, enhance the transfer of features, extract deeper features and multi-scale features, the network structure uses various model structure of the mainstream algorithms. Bagging training method is used in the training process, that is, different learners use different data sets to ensure the learning differences. Finally, the prediction result of all classifiers is used to get the final image target recognition according to the decision algorithm. The algorithm is simulated with of the open data set of cifar-10. The experimental results show that the proposed algorithm has a high recognition accuracy. The recognition rate of the test set reaches 98.89% and the recognition result is securer and reliable.