An Adaptive Version of the Boost by Majority Algorithm
Machine Learning
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
CO3 for ultra-fast and accurate interactive segmentation
Proceedings of the international conference on Multimedia
A Numerical Study of the Bottom-Up and Top-Down Inference Processes in And-Or Graphs
International Journal of Computer Vision
Feature set search space for fuzzyboost learning
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Cloosting: clustering data with boosting
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
EGSR'08 Proceedings of the Nineteenth Eurographics conference on Rendering
International Journal of Computer Vision
A boosting approach for the simultaneous detection and segmentation of generic objects
Pattern Recognition Letters
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SpatialBoost extends AdaBoost to incorporate spatial reasoning. We demonstrate the effectiveness of SpatialBoost on the problem of interactive image segmentation. Our application takes as input a tri-map of the original image, trains SpatialBoost on the pixels of the object and the background and use the trained classifier to classify the unlabeled pixels. The spatial reasoning is introduced in the form of weak classifiers that attempt to infer pixel label from the pixel labels of surrounding pixels, after each boosting iteration. We call this variant of AdaBoost — SpatialBoost. We then extend the application to work with “GrabCut”. In GrabCut the user casually marks a rectangle around the object, instead of tediously marking a tri-map, and we pose the segmentation as the problem of learning with outliers, where we know that only positive pixels (i.e. pixels that are assumed to belong to the object) might be outliers and in fact should belong to the background.