PT Unknown
AU David Aldavert
   Ricardo Toledo
   Arnau Ramisa
   Ramon Lopez de Mantaras
TI Efficient Object Pixel-Level Categorization using Bag of Features:  Advances in Visual Computing
BT 5th International Symposium on  Visual  Computing
PY 2009
BP 44–55
VL 5875
DI 10.1007/978-3-642-10331-5_5
AB 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.
ER