PT Unknown
AU David Aldavert
   Arnau Ramisa
   Ramon Lopez de Mantaras
   Ricardo Toledo
TI Fast and Robust Object Segmentation with the Integral Linear Classifier
BT 23rd IEEE Conference on Computer Vision and Pattern Recognition
PY 2010
BP 1046–1053
DI 10.1109/CVPR.2010.5540098
AB We propose an efficient method, built on the popular Bag of Features approach, that obtains robust multiclass pixel-level object segmentation of an image in less than 500ms, with results comparable or better than most state of the art methods. We introduce the Integral Linear Classifier (ILC), that can readily obtain the classification score for any image sub-window with only 6 additions and 1 product by fusing the accumulation and classification steps in a single operation. In order to design a method as efficient as possible, our building blocks are carefully selected from the quickest in the state of the art. More precisely, we evaluate the performance of three popular local descriptors, that can be very efficiently computed using integral images, and two fast quantization methods: the Hierarchical K-Means, and the Extremely Randomized Forest. Finally, we explore the utility of adding spatial bins to the Bag of Features histograms and that of cascade classifiers to improve the obtained segmentation. Our method is compared to the state of the art in the difficult Graz-02 and PASCAL 2007 Segmentation Challenge datasets.
ER