COMPARATIVE STUDY ON GRAPH CUTS FOR IMAGE SEGMENTATION
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Abstract
Combinatorial graph cut algorithms have been successfully applied to a wide range of problems in computer vision and graphics. In this paper we have analyzed mainly four techniques Graph cuts (GC), Iterative Graph Cuts (IGC), Multi-label Random walker (RW) and Lazy Snapping (LS). Graph Cut techniques are used for completely automatic high-level grouping of image pixels. Iterative Graph Cut technique allows some sort of user interaction for extracting objects from a complex background. Random Walker algorithm requires user specific labels and produce a segmentation where each segment is connected to a labeled pixel. Lazy Snapping provides instant visual feedback, snapping the cutout contour to the true object boundary efficiently despite the presence of ambiguous or low contrast edges.
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2008 Ieee 16th Signal Processing Communication and Applications Conference, 2008
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IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 2008
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Statistical Significance Based Graph Cut Segmentation for Shrinking Bias
Lecture Notes in Computer Science, 2011
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.
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