Application of Multi-Column Heterogeneous Convolutional Neural Networks in image classification

Abstract

Image classification is an important research direction of computer vision. Convolutional neural network is a deep feedforward neural network model. It uses the deep learning idea and shows good performance in multiple image classification fields such as speech recognition, face recognition, motion analysis, and medical diagnosis. However, a single-structure convolutional neural network is prone to overfitting problems. The main reason for the overfitting problem is that the learning model overfits the training set and results in the lack of generalization performance, which affects the feature extraction and judgment of the test set.

This paper presents a structure model for Multi-Column Heterogeneous Convolutional Neural Networks. Multi-Column Heterogeneous Convolutional Neural Networks are used in image classification. We construct several convolutional neural networks with different structures by setting different size of convolution kernels and different number of feature maps. Image features are learned from multiple perspectives. Each convolutional neural network model is trained on the training set, and the different network models are fitted to the training set. Finally, through the sliding window, the output of each network is fused to obtain a relatively better prediction result. Experiments show that Multi-Column Heterogeneous Convolutional Neural Networks reduce the overfitting problem to a certain extent, and the accuracy of object recognition is improved compared to the single structure convolutional neural network.