Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/43965
Título: Features extraction techniques for pollen grain classification
Autores/as: del Pozo-Baños, Marcos
Ticay-Rivas, Jaime R.
Alonso, Jesús B. 
Travieso, Carlos M. 
Clasificación UNESCO: 3307 Tecnología electrónica
Palabras clave: Pollen grain identificationPlant biometricPattern recognitionPalynology
Fecha de publicación: 2015
Editor/a: 0925-2312
Publicación seriada: Neurocomputing 
Conferencia: IEEE 17th International Conference on Intelligent Engineering Systems (INES) 
Resumen: 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
URI: http://hdl.handle.net/10553/43965
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2014.05.085
Fuente: Neurocomputing[ISSN 0925-2312],v. 150, p. 377-391
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