Application of mobile edge computing combined with convolutional neural network deep learning in image analysis

This paper aims to improve the accuracy and efficiency of image aesthetic classification in environmental art design and provide a more professional and convenient method. Based on the Mobile Edge Computing (MEC) and Convolution Neural Network (CNN) Deep Learning (DL) algorithm, the current situation and shortcomings of the existing image aesthetic classification are analyzed. Thereupon, the MEC technology is combined with CNN, and the MEC-based Image Recognition (IR) architecture and parallel Deep CNN-based aesthetic evaluation method are proposed. Then, the environmental art design images are analyzed and classified using the proposed method. Experiments are designed to verify the performance of the proposed methods. The results show that the average Response Time (RT) of different images recognition under the mobile cellular network and WiFi network is more than 2000 ms and less than 1000 ms, respectively. Comparison of the transmission speeds given different images indicates that the IR RT under the proposed MEC Hierarchical Discriminant Analysis (MECHDA) architecture is faster than the other three: the MECHDA architecture occupies a smaller bandwidth, with a constant 1kB image transmission traffic. Additionally, the aesthetic classification accuracy of the proposed model in A large-scale database for aesthetic visual analysis data set has reached 85.3%. In the data set of the CUHK Photo Quality Dataset (CUNK-PQ), the accuracy of aesthetic classification has reached 93%. The environmental art design pre-analysis and beauty collection classification method proposed enables the computer to help humans make more accurate image aesthetic analysis and classification. It improves the efficiency and accuracy of image aesthetic classification and provides a reference for research in related fields.