Features extraction techniques for pollen grain classification

•A new set of features for pollen grain classification.•A comprehensive study on the reliability of the most used features extractors.•This study contributes new knowledge on the pollen grains classification field.

An extensive study on pollen grain identification is presented in this work. A combination of geometrical and texture characteristics is proposed as pollen grain discriminative features as well as the usage of the most popular feature extraction techniques. Multi-Layer Neural Network and Least Square Support Vector Machines (LS-SVM) with Radial Basis Function were used as classifier systems. K-fold and hold-out cross-validation techniques were applied in order to achieve reliable results. When testing with a 17-species database, the combination of the proposed set of features processed by Linear Discriminant Analysis and the LS-SVM has provided the best performance, reaching a 94.92%±0.61 of success rate. Subsequently, the combination of both classifier methods provided better results, achieving 95.27%±0.49 of accuracy.