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Pollen allergy is a common seasonal disease. Current automatic pollen classification methods have high accuracy for high-quality images. However, the imaging process of pollen can produce defocusing blur, and some pollen characteristics have subtle differences, which limits the accuracy of the methods. To address this, we introduce a supervised contrastive learning method to fine-tune the pre-trained classification network and optimize its feature representation ability. We combined multilayer scan images of the same pollen into positive example pairs, and with multilayer scan images of other classes of pollen into negative example pairs. This can learn more similar features of the same class samples and differences of different class samples. We establish a pollen dictionary based on feature representation, compare new test samples with pollen grains in the dictionary, and obtain the final pollen class through weighted voting. Experimental results show that our method is effective for distinguishing easily confused pollen grains. © 2024 ACM.
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年份: 2024
页码: 7-11
语种: 英文
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