A Fiber Image Classification Strategy Based on Key Module Localization

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dc.contributor.authorJi, Ya Tuen_US
dc.contributor.authorXue, Xiangen_US
dc.contributor.authorLiu, Yangen_US
dc.contributor.authorXu, H. T.en_US
dc.contributor.authorRen, Q. D. E. J.en_US
dc.contributor.authorShi, B.en_US
dc.contributor.authorWu, N. E.en_US
dc.contributor.authorLu, M.en_US
dc.contributor.authorXu, X. X.en_US
dc.contributor.authorWang, L.en_US
dc.contributor.authorDai, L. J.en_US
dc.contributor.authorYao, M. M.en_US
dc.contributor.authorLi, X. M.en_US
dc.contributor.editorChen, Renjieen_US
dc.contributor.editorRitschel, Tobiasen_US
dc.contributor.editorWhiting, Emilyen_US
dc.date.accessioned2024-10-13T18:05:58Z
dc.date.available2024-10-13T18:05:58Z
dc.date.issued2024
dc.description.abstractTraditional image classification approach divides the fiber image into several non overlapping patches during the embedding stage. However, for fine-grained image data, this rough method makes the model lack the ability to model locally within each patch. In addition, the overall proportion of fiber features is always small and densely distributed, and irrelevant interference noise occupies the vast majority of the image. Therefore, this paper proposes a strategy to address the above issues. Firstly, ResNeXt-50 is used to obtain prior information such as inductive bias and translation invariance. Then, by introducing a lightweight Coordinate Attention, focus is achieved on the inside of the fibers rather than background information. Finally, this information is used as input to the Grad-CAM module to accurately identify the fiber interior regions of interest. The proposed approach has significant advantages over multiple strong baseline models on the test data provided by the National Fiber Quality Testing Center, as it can effectively learn fiber skeleton features and achieve finer grained modeling.en_US
dc.description.sectionheadersPosters
dc.description.seriesinformationPacific Graphics Conference Papers and Posters
dc.identifier.doi10.2312/pg.20241323
dc.identifier.isbn978-3-03868-250-9
dc.identifier.pages2 pages
dc.identifier.urihttps://doi.org/10.2312/pg.20241323
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/pg20241323
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies → Image processing; Shape analysis; Neural networks
dc.subjectComputing methodologies → Image processing
dc.subjectShape analysis
dc.subjectNeural networks
dc.titleA Fiber Image Classification Strategy Based on Key Module Localizationen_US
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