Evaluation of random forests on large-scale classification problems using a bag-of-visual-words representation
Document typeConference report
Rights accessOpen Access
European Commission's projectCHIST-ERA II - European Coordinated Research on Long-term Challenges in Information and Communication Sciences and Technologies - II (EC-FP7-287654)
Random Forest is a very efficient classification method that has shown success in tasks like image segmentation or object detection, but has not been applied yet in large-scale image classification scenarios using a Bag-of-Visual-Words representation. In this work we evaluate the performance of Random Forest on the ImageNet dataset, and compare it to standard approaches in the state-of-the-art.
CitationSoler, X., Ramisa, A., Torras, C. Evaluation of random forests on large-scale classification problems using a bag-of-visual-words representation. A: Catalan Conference on Artificial Intelligence. "Frontiers in Artificial Intelligence and Applications". Barcelona: 2014, p. 273-276.