Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/16113
Título: Exploring the use of local descriptors for fish recognition in LifeCLEF 2015
Autores/as: Cabrera-Gámez, Jorge 
Castrillón-Santana, Modesto 
Domínguez-Brito, Antonio 
Hernández Sosa, José Daniel 
Isern-González, Josep 
Lorenzo-Navarro, Javier 
Clasificación UNESCO: 120304 Inteligencia artificial
120325 Diseño de sistemas sensores
Fecha de publicación: 2015
Publicación seriada: CEUR Workshop Proceedings 
Conferencia: 16th Conference and Labs of the Evaluation Forum, CLEF 2015 
Resumen: This paper summarizes the proposal made by the SIANI team for the LifeCLEF 2015 Fish task. The approach makes use of standard detection techniques, applying a multiclass SVM based classifier on large enough Regions Of Interest (ROIs) automatically extracted from the provided video frames. The selection of the detection and classification modules is based on the best performance achieved for the validation dataset consisting of 20 annotated videos. For that dataset, the best classification achieved for an ideal detection module, reaches an accuracy around 40%.
URI: http://hdl.handle.net/10553/16113
ISSN: 1613-0073
Fuente: CEUR Workshop Proceedings[ISSN 1613-0073],v. 1391
Colección:Actas de congresos
miniatura
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