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.