Robust interactive segmentation via coloring
Proceedings of the 1st International Workshop on Visual Interfaces for Ground Truth Collection in Computer Vision Applications
Error-tolerant interactive image segmentation using dynamic and iterated graph-cuts
Proceedings of the 2nd ACM international workshop on Interactive multimedia on mobile and portable devices
Geodesic saliency using background priors
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
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Drawing a box around an intended segmentation target has become both a popular user interface and a common output for learning-driven detection algorithms. Despite the ubiquity of using a box to define a segmentation target, it is unclear in the literature whether a box is sufficient to define a unique segmentation or whether segmentation from a box is ill-posed without higher-level (semantic) knowledge of the intended target. We examine this issue by conducting a study of 14 subjects who are asked to segment a boxed target in a set of 50 real images for which they have no semantic attachment. We find that the subjects do indeed perceive and trace almost the same segmentations as each other, despite the inhomogeneity of the image intensities, irregular shapes of the segmentation targets and weakness of the target boundaries. Since the subjects produce the same segmentation, we conclude that the problem is well-posed and then provide a new segmentation algorithm from a box which achieves results close to the perceived target.