Automatic Selection of Stochastic Watershed Hierarchies
This work addresses the challenge of automating segmentation for homogeneous image sets, though it appears incremental as it builds on existing stochastic watershed methods.
The paper tackles the problem of automatic image segmentation by selecting the best stochastic watershed hierarchy and cut level for a given set of images, resulting in improved image simplification as demonstrated on real-life datasets.
The segmentation, seen as the association of a partition with an image, is a difficult task. It can be decomposed in two steps: at first, a family of contours associated with a series of nested partitions (or hierarchy) is created and organized, then pertinent contours are extracted. A coarser partition is obtained by merging adjacent regions of a finer partition. The strength of a contour is then measured by the level of the hierarchy for which its two adjacent regions merge. We present an automatic segmentation strategy using a wide range of stochastic watershed hierarchies. For a given set of homogeneous images, our approach selects automatically the best hierarchy and cut level to perform image simplification given an evaluation score. Experimental results illustrate the advantages of our approach on several real-life images datasets.