@article{,
title= {POLEN23E: image dataset for the Brazilian Savannah pollen types},
keywords= {},
author= {Ariadne Barbosa Gonçalves and Junior Silva Souza and Gercina Gonçalves da Silva and Marney Pascoli Cereda and Arnildo Pott and Marco Hiroshi Naka and Hemerson Pistori },
abstract= {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.

https://i.imgur.com/P2bNuVi.png

Citation:
Gonçalves AB, Souza JS, Silva GGd, Cereda MP, Pott A, Naka MH, et al. (2016) Feature Extraction and Machine Learning for the Classification of Brazilian Savannah Pollen Grains. PLoS ONE 11(6): e0157044. https://doi.org/10.1371/journal.pone.0157044 The link for the dataset is: http://dx.doi.org/10.6084/m9.figshare.1525086.
},
terms= {},
license= {Creative Commons Attribution License},
superseded= {},
url= {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0157044}
}