A bark recognition algorithm for plant classification using a least square support vector machine

Título:	A bark recognition algorithm for plant classification using a least square support vector machine
Autores/as:	Blaanco, Luis J. Travieso, Carlos M. Quinteiro, Jose M. Hernandez, Pablo V. Dutta, Malay Kishore Singh, Anushikha
Clasificación UNESCO:	3307 Tecnología electrónica
Palabras clave:	Classification algorithm Support vector machines Discrete wavelet transforms Biology computing Pattern classification
Fecha de publicación:	2017
Publicación seriada:	2016 Ninth International Conference On Contemporary Computing (Ic3)
Conferencia:	9th International Conference on Contemporary Computing, IC3 2016
Resumen:	In this paper, a bark recognition algorithm for plant classification is presented. A Least-Square Support Vector Machine (LSSVM) with image and data processing techniques is used to implement a general purpose automated classifier. Using a data base of 40 sections of photographs taken of each of the 23 species, we applied an algorithm to homogenize the illumination of the images. After applying it, we obtained a 256-elements array from the Local Binary Pattern (LBP) histogram. Each element of the array was introduced in the LSSVM for classification. The success rate of the resultant recognizer is 82.38%.
URI:	http://hdl.handle.net/10553/46034
ISBN:	9781509032518
ISSN:	2572-6110
DOI:	10.1109/IC3.2016.7880233
Fuente:	2016 9th International Conference on Contemporary Computing, IC3 2016 (7880233)
Colección:	Actas de congresos

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