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

Sevillano Plaza, Victor. (2019). Improving classication of pollen grain images of the POLEN23E dataset deep learning Master Thesis, Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial.

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Título Improving classication of pollen grain images of the POLEN23E dataset deep learning
Autor(es) Sevillano Plaza, Victor
Abstract 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%.
Notas adicionales Trabajo de Fin de Máster. Máster Universitario en I.A. Avanzada: Fundamentos, Métodos y Aplicaciones. UNED
Materia(s) Ingeniería Informática
Editor(es) Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial.
Director/Tutor Aznarte Mellado, Jose Luis
Fecha 2019-09-19
Formato application/pdf
Identificador bibliuned:master-ETSInformatica-IAA-Vmsevillano
http://e-spacio.uned.es/fez/view/bibliuned:master-ETSInformatica-IAA-Vmsevillano
Idioma eng
Versión de la publicación acceptedVersion
Nivel de acceso y licencia http://creativecommons.org/licenses/by-nc-nd/4.0
info:eu-repo/semantics/openAccess
Tipo de recurso master Thesis
Tipo de acceso Acceso abierto

 
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Creado: Mon, 19 Oct 2020, 17:57:50 CET