Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM SIGGRAPH 2004 Papers
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Inducing semantic segmentation from an example
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Cosegmentation revisited: models and optimization
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
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Interactive image segmentation is a powerful paradigm that allows users to direct the segmentation algorithm towards a desired output. However, marking scribbles on multiple images is a cumbersome process. Recent works show that statistics collected from user input in a single image can be shared among a group of related images to perform interactive cosegmentation. Most works use a naive heuristic of requesting the user input on a random image from the group. We show that in practice, selecting the right image to scribble on is critical to the resulting segmentation quality. In this paper, we address the problem of Seed Image Selection, i.e., deciding which image among a group of related images should be presented to the user for scribbling. We formulate our approach as a classification problem and show that our approach outperforms the naive heuristic used by other works.