@inproceedings{1489cab300df477089b1d9895a724613,
title = "Evaluation of random forests on large-scale classification problems using a bag-of-visual-words representation",
abstract = "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.",
keywords = "classifier forest, large-scale image classification, random forests",
author = "Xavier Sol{\'e} and Arnau Ramisa and Carme Torras",
year = "2014",
doi = "10.3233/978-1-61499-452-7-273",
language = "English",
series = "Frontiers in Artificial Intelligence and Applications",
publisher = "IOS Press BV",
pages = "273--276",
editor = "Lledo Museros and Oriol Pujol and Nuria Agell",
booktitle = "Artificial Intelligence Research and Development - Recent Advances and Applications",
address = "Netherlands",
note = "17th International Conference of the Catalan Association for Artificial Intelligence, CCIA 2014 ; Conference date: 22-10-2014 Through 24-10-2014",
}