Texture in classification of pollen grain images

Abstract

In this paper we present a model for classification of pollen grain images based on surface texture. The surface textures of pollens are extracted using different models like Wavelet, Gabor, Local Binary Pattern (LBP), Gray Level Difference Matrix (GLDM) and Gray Level Co-Occurrence Matrix (GLCM) and combination of these features. The Nearest Neighbor (NN) classifier is adapted for classification. Unlike other existing contemporary works which are designed for a specific family or for one or few different families, the proposed model is designed independent of families of pollen grains. Experimentations on a dataset containing pollen grain images of about 50 different families totally 419 images of 18 classes have been conducted to demonstrate the performance of the proposed model. A classification rate up to 91.66 % is achieved when Gabor wavelet features are used. © 2013 Springer.