Connecting Quality Metrics to Deep Learning Accuracy for Image Fusion Methods
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Joo, H. | - |
dc.contributor.author | Choi, Y. | - |
dc.contributor.author | Park, J. | - |
dc.contributor.author | Lim, C.H. | - |
dc.contributor.author | Yang, H.J. | - |
dc.date.accessioned | 2023-12-22T10:02:25Z | - |
dc.date.available | 2023-12-22T10:02:25Z | - |
dc.date.issued | 2022-10 | - |
dc.identifier.issn | 2162-1233 | - |
dc.identifier.uri | https://www.kriso.re.kr/sciwatch/handle/2021.sw.kriso/9602 | - |
dc.description.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. | - |
dc.format.extent | 6 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | IEEE Computer Society | - |
dc.title | Connecting Quality Metrics to Deep Learning Accuracy for Image Fusion Methods | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.1109/ICTC55196.2022.9952709 | - |
dc.identifier.scopusid | 2-s2.0-85143254023 | - |
dc.identifier.bibliographicCitation | International Conference on ICT Convergence, v.2022-October, pp 142 - 147 | - |
dc.citation.title | International Conference on ICT Convergence | - |
dc.citation.volume | 2022-October | - |
dc.citation.startPage | 142 | - |
dc.citation.endPage | 147 | - |
dc.type.docType | Conference Paper | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | Accuracy Prediction | - |
dc.subject.keywordAuthor | Convolutional Neural Network | - |
dc.subject.keywordAuthor | Image Fusion | - |
dc.subject.keywordAuthor | Image Preprocessing | - |
dc.subject.keywordAuthor | Quality Assessment | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(34103) 대전광역시 유성구 유성대로1312번길 32042-866-3114
COPYRIGHT 2021 BY KOREA RESEARCH INSTITUTE OF SHIPS & OCEAN ENGINEERING. ALL RIGHTS RESERVED.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.