"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
What Metrics Can Be Approximated by Geo-Cuts, Or Global Optimization of Length/Area and Flux
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
Adaptive Contextual Energy Parameterization for Automated Image Segmentation
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Dynamic Programming and Graph Algorithms in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive regularization parameter for graph cut segmentation
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part I
Feature prominence-based weighting scheme for video tracking
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
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Graph cut algorithms are very popular in image segmentation approaches. However, the detailed parts of the foreground are not segmented well in graph cut minimization.There are basically two reasons of inadequate segmentations: (i) Data - smoothness relationship of graph energy. (ii) Shrinking bias which is the bias towards shorter paths. This paper improves the foreground segmentation by integrating the statistical significance measure into the graph energy minimization. Significance measure changes the relative importance of graph edge weights for each pixel. Especially at the boundary parts, the data weights take more significance than the smoothness weights. Since the energy minimization approach takes into account the significance measure, the minimization algorithm produces better segmentations at the boundary regions. Experimental results show that the statistical significance measure makes the graph cut algorithm less prone to bias towards shorter paths and better at boundary segmentation.