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A Seeded Image Segmentation Framework Unifying Graph Cuts And Random Walker Which Yields A New Algorithm (2007)
| Content Provider | CiteSeerX |
|---|---|
| Author | Sinop, Ali Kemal Grady, Leo |
| Description | This content is published in/by ICCV |
| Abstract | In this work, we present a common framework for seeded image segmentation algorithms that yields two of the leading methods as special cases- The Graph Cuts and the Random Walker algorithms. The formulation of this common framework naturally suggests a new, third, algorithm that we develop here. Specifically, the former algorithms may be shown to minimize a certain energy with respect to either an ℓ1 or an ℓ2 norm. Here, we explore the segmentation algorithm defined by an ℓ ∞ norm, provide a method for the optimization and show that the resulting algorithm produces an accurate segmentation that demonstrates greater stability with respect to the number of seeds employed than either the Graph Cuts or Random Walker methods. |
| File Format | |
| Publisher Date | 2007-01-01 |
| Access Restriction | Open |
| Subject Keyword | Random Walker Graph Cut Common Framework Former Algorithm Seeded Image Segmentation Certain Energy Random Walker Method Random Walker Algorithm Accurate Segmentation Segmentation Algorithm |
| Content Type | Text |
