An efficient hierarchical graph based image segmentation
This work addresses the problem of parameter tuning and lack of hierarchy in image segmentation for computer vision applications, but it is incremental as it builds on an existing criterion.
The paper tackles the challenge of creating hierarchical image segmentations by proposing a graph-based method that uses a criterion from Felzenzwalb and Huttenlocher, demonstrating efficiency, ease of use, and robustness on real and synthetic images.
Hierarchical image segmentation provides region-oriented scalespace, i.e., a set of image segmentations at different detail levels in which the segmentations at finer levels are nested with respect to those at coarser levels. Most image segmentation algorithms, such as region merging algorithms, rely on a criterion for merging that does not lead to a hierarchy, and for which the tuning of the parameters can be difficult. In this work, we propose a hierarchical graph based image segmentation relying on a criterion popularized by Felzenzwalb and Huttenlocher. We illustrate with both real and synthetic images, showing efficiency, ease of use, and robustness of our method.