Image quality classification algorithm based on InceptionV3 and SVM

¹ School of Mechanical and Electrical Engineering and Automation, Shanghai University, Shanghai, China
² Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, China

^* Corresponding author: liulz@sari.ac.cn

Abstract

In this work we investigate the use of deep learning for image quality classification problem. We use a pre-trained Convolutional Neural Network (CNN) for image description, and the Support Vector Machine (SVM) model is trained as an image quality classifier whose inputs are normalized features extracted by the CNN model. We report on different design choices, ranging from the use of various CNN architectures to the use of features extracted from different layers of a CNN model. To cope with the problem of a lack of adequate amounts of distorted picture data, a novel training strategy of multi-scale training, which is selecting a new image size for training after several batches, combined with data augmentation is introduced. The experimental results tested on the actual monitoring video images shows that the proposed model can accurately classify distorted images.