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Improving classication of pollen grain images of the POLEN23E dataset deep learning

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dc.contributor.authorSevillano Plaza, Victor
dc.contributor.directorAznarte Mellado, José Luis
dc.date.accessioned2024-05-20T12:33:51Z
dc.date.available2024-05-20T12:33:51Z
dc.date.issued2019-09-19
dc.description.abstractIn 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%.en
dc.description.versionversión final
dc.identifier.urihttps://hdl.handle.net/20.500.14468/14521
dc.language.isoen
dc.publisherUniversidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial.
dc.relation.centerFacultades y escuelas::E.T.S. de Ingeniería Informática
dc.relation.degreeMáster Universitario en I.A. Avanzada: Fundamentos, Métodos y Aplicaciones
dc.relation.departmentInteligencia Artificial
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.titleImproving classication of pollen grain images of the POLEN23E dataset deep learninges
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dc.typemaster thesisen
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