The Pattern Recognition in Cattle Brand using Bag of Visual Words and Support Vector Machines Multi-Class

• Autores: Carlos Silva, Daniel Welfer, Cláudia Dornelles
• Localización: Inteligencia artificial: Revista Iberoamericana de Inteligencia Artificial, ISSN-e 1988-3064, ISSN 1137-3601, Vol. 21, Nº. 61, 2018 (Ejemplar dedicado a: Inteligencia Artificial (June 2018)), págs. 1-13
• Idioma: inglés
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• Resumen

  • The recognition images of cattle brand in an automatic way is a necessity to governmental organs responsible for this activity. To help this process, this work presents a method that consists in using Bag of Visual Words for extracting of characteristics from images of cattle brand and Support Vector Machines Multi-Class for classification. This method consists of six stages: a) select database of images; b) extract points of interest (SURF); c) create vocabulary (K-means); d) create vector of image characteristics (visual words); e) train and sort images (SVM); f) evaluate the classification results. The accuracy of the method was tested on database of municipal city hall, where it achieved satisfactory results, reporting 86.02% of accuracy and 56.705 seconds of processing time, respectively.