Improving pollen classification with less training effort

Abstract

The pollen grains of different plant taxa exhibit various shapes and sizes. This structural diversity has made the identification and classification of pollen grains an important tool in many fields. Despite the myriad of applications, the classification of pollen grains is still a tedious and time-consuming process that must be performed by highly skilled specialists. In this paper, we propose an automatic classification method to discriminate pollen grains coming from a variety of taxonomic types. First, we develop a new feature that captures the spikes of pollen to improve the classification accuracy. Second, we take advantage of the classification rules extracted from the existing pollen types and apply them to the new types. Third, we introduce a new selection criterion to obtain the most valuable training samples from the unlabeled data and therefore reduce the number of needed training samples. Our experiment demonstrates that the proposed method reduces the training effort of a human expert up to 80% compared to other classification methods while achieving 92% accuracy in pollen classification.