Open AccessFeature Extraction and Machine Learning for the Classification of Brazilian Savannah Pollen Grains
Author(s) -
Ariadne Barbosa Gonçalves,
Junior Silva Souza,
Gercina Gonçalves da Silva,
Marney Pascoli Cereda,
Arnildo Pott,
Marco Hiroshi Naka,
Hemerson Pistori
Publication year - 2016
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0157044
Subject(s) - pollen , palynology , computer science , feature (linguistics) , artificial intelligence , pattern recognition (psychology) , feature extraction , task (project management) , machine learning , botany , biology , engineering , linguistics , philosophy , systems engineering
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.