Power watersheds: a new image segmentation framework extending graph cuts, random walker and optimal spanning forest

In this work, we extend a common framework for seeded image segmentation that includes the graph cuts, ran- dom walker, and shortest path optimization algorithms. Viewing an image as a weighted graph, these algorithms can be expressed by means of a common energy func- tion with differing choices of a parameter q acting as an exponent on the differences between neighboring nodes. Introducing a new parameter p that fixes a power for the edge weights allows us to also include the optimal span- ning forest algorithm for watersheds in this same frame- work. We then propose a new family of segmentation algorithms that fixes p to produce an optimal spanning forest but varies the power q beyond the usual water- shed algorithm, which we term power watersheds. Placing the watershed algorithm in this energy mini- mization framework also opens new possibilities for us- ing unary terms in traditional watershed segmentation and using watersheds to optimize more general models of us...

• Type: Conference
• Year: 2009
• Where: ICCV
• Authors: Camille Couprie, Leo Grady, Laurent Najman, Hugues Talbot
• Keywords: Computer Vision, ICCV 2009, Power Watersheds