Markov Random Fields with Efficient Approximations
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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
Order-Preserving Moves for Graph-Cut-Based Optimization
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Graph cut based inference with co-occurrence statistics
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Generalized ordering constraints for multilabel optimization
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Bridging the gap between low-level and high-level image analysis has been a central challenge in computer vision throughout the last decades. In this article I will point out a number of recent developments in low-level image analysis which open up new possibilities to bring together concepts of high-level and low-level vision. The key observation is that numerous multi@?label optimization problems can nowadays be efficiently solved in a near-optimal manner, using either graph-theoretic algorithms or convex relaxation techniques. Moreover, higher-level semantic knowledge can be learned and imposed on the basis of such multi@?label formulations.