MATEC Web Conf.
Volume 277, 20192018 International Joint Conference on Metallurgical and Materials Engineering (JCMME 2018)
|Number of page(s)||7|
|Section||Data and Signal Processing|
|Published online||02 April 2019|
Image quality classification algorithm based on InceptionV3 and SVM
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: email@example.com
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.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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