Pollen grain classification has a remarkable role in many fields from medicine to biology and agronomy. Indeed, automatic pollen grain classification is an important task for all related applications and areas. This work presents the first large-scale pollen grain image dataset, including more than 13 thousands objects. After an introduction to the problem of pollen grain classification and its motivations, the paper focuses on the employed data acquisition steps, which include aerobiological sampling, microscope image acquisition, object detection, segmentation and labelling. Furthermore, a baseline experimental assessment for the task of pollen classification on the built dataset, together with discussion on the achieved results, is presented.
@article{arxiv.2007.04690,
title = {Pollen13K: A Large Scale Microscope Pollen Grain Image Dataset},
author = {Sebastiano Battiato and Alessandro Ortis and Francesca Trenta and Lorenzo Ascari and Mara Politi and Consolata Siniscalco},
journal= {arXiv preprint arXiv:2007.04690},
year = {2020}
}
Comments
This paper is a preprint of a paper accepted at the IEEE International Conference on Image Processing 2020