Publicación: Improving classication of pollen grain images of the POLEN23E dataset deep learning
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2019-09-19
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info:eu-repo/semantics/openAccess
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Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial.
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In palynology, the visual classication of pollen grains from dierent species is a hard task which is usually tackled by human operators using microscopes. Its complete automatization would save a high quantity of resources and provide valuable improvements especially for allergy-related information systems, but also for other application elds as paleoclimate reconstruction, quality control of honey-based products, collection of evi- dences in criminal investigations or fabric dating and tracking. This paper presents three state-of-the-art deep learning classication methods applied to the recently published POLEN23E image dataset. The three methods make use of convolutional neural networks: the rst one is strictly based on the idea of transfer learning, the second one is based on feature extraction and the third one represents a hybrid approach, combining transfer learning and feature extraction. The results from the three methods are indeed very good, reaching over 99% correct classication rates in the training set of images and over 96% in images not previously seen by the models where other authors reported around 70%.
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Facultades y escuelas::E.T.S. de Ingeniería Informática
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Inteligencia Artificial