Improving classification of pollen grain images of the POLEN23E dataset through three different applications of deep learning convolutional neural networks.

Autor: Víctor Sevillano, José L Aznarte
Jazyk: angličtina
Rok vydání: 2018
Předmět:
Zdroj: PLoS ONE, Vol 13, Iss 9, p e0201807 (2018)
Druh dokumentu: article
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0201807
Popis: In palynology, the visual classification of pollen grains from different 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 fields as paleoclimate reconstruction, quality control of honey based products, collection of evidences in criminal investigations or fabric dating and tracking. This paper presents three state-of-the-art deep learning classification methods applied to the recently published POLEN23E image dataset. The three methods make use of convolutional neural networks: the first 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 97% correct classification rates in images not previously seen by the models, where other authors reported around 70.
Databáze: Directory of Open Access Journals
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