A Fiber Image Classification Strategy Based on Key Module Localization
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Traditional 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.
Description
CCS Concepts: Computing methodologies → Image processing; Shape analysis; Neural networks
@inproceedings{10.2312:pg.20241323,
booktitle = {Pacific Graphics Conference Papers and Posters},
editor = {Chen, Renjie and Ritschel, Tobias and Whiting, Emily},
title = {{A Fiber Image Classification Strategy Based on Key Module Localization}},
author = {Ji, Ya Tu and Xue, Xiang and Dai, L. J. and Yao, M. M. and Li, X. M. and Liu, Yang and Xu, H. T. and Ren, Q. D. E. J. and Shi, B. and Wu, N. E. and Lu, M. and Xu, X. X. and Wang, L.},
year = {2024},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-250-9},
DOI = {10.2312/pg.20241323}
}