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.

花粉物种与类型的分类，是法医孢粉学、考古孢粉学及蜜孢粉学等诸多领域的重要研究任务。本文首次发布了针对巴西稀树草原花粉类型的带标注图像数据集，可用于训练与测试基于计算机视觉的自动花粉分类器。本数据集基于涵盖23个花粉类型的805张花粉图像，确立了首个人类与计算机的基准性能基线。为评估计算机分类性能，本文实现了三种特征提取器与四种机器学习技术的组合方案，并对其进行了微调与测试。本文同时呈现了上述测试的全部实验结果。