TY  - CONF
AU  - David Aldavert
AU  - Ricardo Toledo
AU  - Arnau Ramisa
AU  - Ramon Lopez de Mantaras
A2  - ISVC
PY  - 2009//
TI  - Efficient Object Pixel-Level Categorization using Bag of Features:  Advances in Visual Computing
BT  - 5th International Symposium on  Visual  Computing
SP  - 44–55
VL  - 5875
PB  - Springer Berlin Heidelberg
N2  - 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.
SN  - 0302-9743
SN  - 978-3-642-10330-8
UR  - https://doi.org/10.1007/978-3-642-10331-5_5
UR  - http://dx.doi.org/10.1007/978-3-642-10331-5_5
N1  - ADAS
ID  - David Aldavert2009
ER  -