Pollen13K: A Large Scale Microscope Pollen Grain Image Dataset
This addresses the need for standardized data in fields like medicine and biology where automated pollen classification is important, though it is incremental as it primarily introduces a new dataset.
The authors tackled the lack of large-scale datasets for pollen grain classification by creating Pollen13K, a dataset with over 13,000 microscope images of pollen grains, and provided baseline classification results.
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.