Orientational Local Binary Pattern Extraction Method for 3D Pollen Image

According the problem that two-dimensional feature cannot describe the internal structure and three-dimensional spatial pixel correlation of pollen image,this paper presents a local binary pattern feature for three-dimensional pollen images recognition.In this method,the feature plane is selected to mark the changing direction of local gray scale,and then the local gray scale vector on the center pixel neighborhood is calculated for constructing the optimal feature plane according to the local gray vector.The local texture feature on the optimal feature plane is extracted to construct the feature matrix.The statistical histogram descriptor of the matrix is finally extracted as the discriminant feature for the three-dimensional pollen image recognition.Experiments are performed on Confocal and Pollenmonitor,two standard European pollen databases and CHMonitor,the China real-collected pollen database.The results demonstrate that the best recognition rate of the algorithm can reach over 95%.Compared with the traditional algorithms,it has better robustness to the scale and attitude change of the pollen images.