Connecting Quality Metrics to Deep Learning Accuracy for Image Fusion Methods
- Authors
- Joo, H.; Choi, Y.; Park, J.; Lim, C.H.; Yang, H.J.
- Issue Date
- 10월-2022
- Publisher
- IEEE Computer Society
- Keywords
- Accuracy Prediction; Convolutional Neural Network; Image Fusion; Image Preprocessing; Quality Assessment
- Citation
- International Conference on ICT Convergence, v.2022-October, pp 142 - 147
- Pages
- 6
- Journal Title
- International Conference on ICT Convergence
- Volume
- 2022-October
- Start Page
- 142
- End Page
- 147
- URI
- https://www.kriso.re.kr/sciwatch/handle/2021.sw.kriso/9602
- DOI
- 10.1109/ICTC55196.2022.9952709
- ISSN
- 2162-1233
- Abstract
- A lot of human-generated data consist of images. While various tasks are becoming automated, reducing the complexity of image processing remains a challenge. At the same time, an image fusion algorithm can be applied before a convolutional neural network (CNN) as an image preprocessing method, where the image fusion combines incoming side-channel images into a single image. Thus, an image fusion can reduce the complexity of the conventional CNN task. However, traditional quality assessment functions (QAFs) for image fusion are a variety of calculation that does not provide a direct clue for the CNN accuracy of interest. In this study, we seek the correlation between QAFs and classification accuracy through CNN. The simulation result by training on differently color-fused CIFAR-10 datasets provides a possible standard to choose an image fusion method in the case of classifying fused images through a CNN. We expect the communication overhead to be decreased while using future image classification models in public. ? 2022 IEEE.
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