Fine-Grained Image Classification for Pollen Grain Microscope Images

Abstract

Pollen classification is an important task in many fields, including allergology, archaeobotany and biodiversity conservation. However, the visual classification of pollen grains is a major challenge due to the difficulty in identifying the subtle variations between the sub-categories of objects. The pollen image analysis process is often time-consuming and require expert evaluations. Even simple tasks, such as image classification or segmentation requires significant efforts from experts in aerobiology. Hence, there is a strong need to develop automatic solutions for microscopy image analysis. These considerations underline the effort to study and develop new efficient algorithms. With the growing interest in Deep Learning (DL), much research efforts have been spent to the development of several approaches to accomplish this task. Hence, this study covers the application of effective Deep Learning methods in combination with Fine-Grained Visual Classification (FGVC) approaches, comparing them with other Deep Learning-based methods from the state-of-art. All experiments were conducted using the dataset Pollen13K, composed of more than 13,000 pollen objects subdivided in 4 classes. The results of experiments confirmed the effectiveness of our proposed pipeline that reached over 97% in terms of accuracy and F1-score.