Feature Extraction and Machine Learning for the Classification of Brazilian Savannah Pollen Grains.
The classification of pollen species and types is an important task in many areas like forensic palynology, archaeological palynology and melissopalynology. This paper presents the first annotated image dataset for the Brazilian Savannah pollen types that can be used to train and test computer vision based automatic pollen classifiers. A first baseline human and computer performance for this dataset has been established using 805 pollen images of 23 pollen types. In order to access the computer performance, a combination of three feature extractors and four machine learning techniques has been implemented, fine tuned and tested. The results of these tests are also presented in this paper.
Department of Biotechnology, INOVISAO, Dom Bosco Catholic University, Campo Grande, Mato Grosso do Sul, Brazil.
Ariadne Barbosa
http://orcid.org/0000-0003-2496-5723
AB
Gonçalves
Department of Computing Science, Universidade Federal de Mato Grosso do Sul, Campo Grande, Mato Grosso do Sul, Brazil.
Junior Silva
JS
Souza
Department of Environmental Science and Agricultural Sustainability, Dom Bosco Catholic University, Campo Grande, Mato Grosso do Sul, Brazil.
Gercina Gonçalves da
GG
Silva
Department of Environmental Science and Agricultural Sustainability, Dom Bosco Catholic University, Campo Grande, Mato Grosso do Sul, Brazil.
Marney Pascoli
MP
Cereda
Laboratory of Botany, Universidade Federal de Mato Grosso do Sul, Campo Grande, Mato Grosso do Sul, Brazil.
Arnildo
A
Pott
Department of Biotechnology, INOVISAO, Dom Bosco Catholic University, Campo Grande, Mato Grosso do Sul, Brazil.|Direction of Research, Extension and Institutional, Federal Institute of Mato Grosso do Sul, Science and Technology. Campo Grande, Mato Grosso do Sul, Brazil.
Marco Hiroshi
MH
Naka
Department of Biotechnology, INOVISAO, Dom Bosco Catholic University, Campo Grande, Mato Grosso do Sul, Brazil.|Department of Computing Science, Universidade Federal de Mato Grosso do Sul, Campo Grande, Mato Grosso do Sul, Brazil.|Department of Environmental Science and Agricultural Sustainability, Dom Bosco Catholic University, Campo Grande, Mato Grosso do Sul, Brazil.
Hemerson
H
Pistori
D001938:Brazil; D065948:Grassland; D000069550:Machine Learning; D011058:Pollen / Q000033:anatomy & histology* / Q000145:classification*