1. Pattern Recognition in Cattle Brand using Bag of Visual Words and Support Vector Machines Multi-Class
- Author
-
Daniel Welfer, Carlos Eduardo da Rosa Silva, and Claudia Dornelles
- Subjects
Vocabulary ,Point of interest ,business.industry ,Computer science ,media_common.quotation_subject ,Pattern recognition ,Class (biology) ,lcsh:QA75.5-76.95 ,030218 nuclear medicine & medical imaging ,Support vector machine ,030507 speech-language pathology & audiology ,03 medical and health sciences ,0302 clinical medicine ,Artificial Intelligence ,Bag-of-words model in computer vision ,Pattern recognition (psychology) ,sort ,lcsh:Electronic computers. Computer science ,Visual Word ,Artificial intelligence ,0305 other medical science ,business ,Software ,media_common - Abstract
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.
- Published
- 2018