Multiple Linear Regression of Multi-class Images in Devices of Internet of Things

Abstract

The correct classification of images is an important application in the monitoring of Internet of things (IoT). In the research of IoT images, a key issue is to recognize multi-class images at a high accuracy. As a result, this paper puts forward a classification method for multi-class images based on multiple linear regression (MLR). Firstly, the convolutional neural network (CNN) was improved to automatically generate a network from the IoT terminals, and used to classify images into disjoint class sets (clusters), which were processed by the subsequently constructed expert network. After that, the MLR was introduced to evaluate the accuracy and robustness of the classification of multi-class images. Finally, the proposed method has been verified on CIFAR-10, CIfar-100 and MNIST, etc. benchmark data sets. Our method was found to outperform other methods in classification, and improve the accuracy of the classic AlexNet by 2%. The research results provide theoretical evidence and lay practical basis for the classification of multi-class IoT images.