@InProceedings{DavidAldavert2009,
author="David Aldavert
and Ricardo Toledo
and Arnau Ramisa
and Ramon Lopez de Mantaras",
title="Efficient Object Pixel-Level Categorization using Bag of Features:  Advances in Visual Computing",
booktitle="5th International Symposium on  Visual  Computing",
year="2009",
publisher="Springer Berlin Heidelberg",
volume="5875",
pages="44--55",
abstract="In this paper we present a pixel-level object categorization method suitable to be applied under real-time constraints. Since pixels are categorized using a bag of features scheme, the major bottleneck of such an approach would be the feature pooling in local histograms of visual words. Therefore, we propose to bypass this time-consuming step and directly obtain the score from a linear Support Vector Machine classifier. This is achieved by creating an integral image of the components of the SVM which can readily obtain the classification score for any image sub-window with only 10 additions and 2 products, regardless of its size. Besides, we evaluated the performance of two efficient feature quantization methods: the Hierarchical K-Means and the Extremely Randomized Forest. All experiments have been done in the Graz02 database, showing comparable, or even better results to related work with a lower computational cost.",
optnote="ADAS",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=1246), last updated on Tue, 29 Oct 2019 09:39:02 +0100",
isbn="978-3-642-10330-8",
issn="0302-9743",
doi="10.1007/978-3-642-10331-5_5",
opturl="https://doi.org/10.1007/978-3-642-10331-5_5"
}