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Connecting Quality Metrics to Deep Learning Accuracy for Image Fusion Methods

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dc.contributor.authorJoo, H.-
dc.contributor.authorChoi, Y.-
dc.contributor.authorPark, J.-
dc.contributor.authorLim, C.H.-
dc.contributor.authorYang, H.J.-
dc.date.accessioned2023-12-22T10:02:25Z-
dc.date.available2023-12-22T10:02:25Z-
dc.date.issued2022-10-
dc.identifier.issn2162-1233-
dc.identifier.urihttps://www.kriso.re.kr/sciwatch/handle/2021.sw.kriso/9602-
dc.description.abstractA 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.extent6-
dc.language영어-
dc.language.isoENG-
dc.publisherIEEE Computer Society-
dc.titleConnecting Quality Metrics to Deep Learning Accuracy for Image Fusion Methods-
dc.typeArticle-
dc.publisher.location미국-
dc.identifier.doi10.1109/ICTC55196.2022.9952709-
dc.identifier.scopusid2-s2.0-85143254023-
dc.identifier.bibliographicCitationInternational Conference on ICT Convergence, v.2022-October, pp 142 - 147-
dc.citation.titleInternational Conference on ICT Convergence-
dc.citation.volume2022-October-
dc.citation.startPage142-
dc.citation.endPage147-
dc.type.docTypeConference Paper-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscopus-
dc.subject.keywordAuthorAccuracy Prediction-
dc.subject.keywordAuthorConvolutional Neural Network-
dc.subject.keywordAuthorImage Fusion-
dc.subject.keywordAuthorImage Preprocessing-
dc.subject.keywordAuthorQuality Assessment-
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