Image Classification Model Based on Deep Learning in Internet of Things

Abstract

In the environment of Internet of Things, the convolutional neural network (CNN) is an important tool and method of image classification. However, the features that are extracted by each layer of CNN are all high dimensional, and the features differ among the layers. In addition, these features contain substantial amounts of redundant information. To prevent the increase in the computational burden and the decline of the model generalization performance that are caused by high dimensionality, this paper proposes an improved image classification algorithm based on deep feature fusion, which designs and builds an 8-layer CNN. In addition, it reduces the dimensionality of the features via the principal component analysis (PCA) dimensionality reduction algorithm and fuses the features that have undergone dimensionality reduction to make the obtained features more typical and differential. The experimental results demonstrate that the proposed algorithm improves the performance of the model and achieves satisfactory accuracy.