Automatic 3D Pollen Recognition Based on Convolutional Neural Network

Abstract

The importance of automatic pollen recognition has been examined in several areas ranging from paleoclimate studies to some daily practice such as pollen hypersensitivity forecasting. This paper attempts to present an automatic 3D pollen image recognition method based on convolutional neural network. To achieve this purpose, high feature dimensions and complex posture transformation should be taken into account. Therefore, this work focuses on a three-part novel approach: constructing spatial local key points to obtain local stable points of pollen images, computing orientational local binary pattern using local stable points as the inputs, and identifying the pollen grains using convolutional neural network as the classifier. Experiments are performed on two standard pollen image datasets: Confocal-E dataset and Pollenmonitor dataset. It is concluded that the proposed approach can effectively extract the features of pollen images and is robust to posture transformation, slight occlusion, and pollution.