Improved Classification of Pollen Texture Images using SVM and MLP

M. Fernández-Delgado, P. Carrión, E. Cernadas, and J.F. Gálvez, and P. Sá-Otero (Spain)


Image analysis, Texture classification, Pollen loads, Support Vector Machine, Multi-Layer Perceptron.


Humans are interested in the determination of the geo graphical origin of honeybee pollen due to its nutritional value and therapeutical benefits. This task is currently be ing developed in a manual way using images from optical microscopy. We have proposed [1, 2] an automatic system for pollen identification, based on its texture classification using a minimum distance classifier. In the present paper, we explore the use of more sophisticated classifiers to im prove the classification stage. Specifically, we apply sev eral well-known classifiers, KNN, Support Vector Machine and Multi-Layer Perceptron, in order to increase the classi fication rate on this problem.

Important Links:

Go Back