Power Watershed: A Unifying Graph-Based Optimization Framework

被引:215
作者
Couprie, Camille [1 ]
Grady, Leo [2 ]
Najman, Laurent [1 ]
Talbot, Hugues [1 ]
机构
[1] Univ Paris Est, Lab Informat Gaspard Monge, Equipe A3SI, ESIEE Paris, F-93160 Noisy Le Grand, France
[2] Siemens Corp Res, Dept Imaging & Visualizat, Princeton, NJ 08540 USA
关键词
Combinatorial optimization; image segmentation; graph cuts; random walker; shortest paths; optimal spanning forests; Markov random fields; IMAGE; SEGMENTATION; ALGORITHM; RECONSTRUCTION; RESTORATION; MINIMUM; MAXIMUM; TREE;
D O I
10.1109/TPAMI.2010.200
中图分类号
TP18 [人工智能理论];
学科分类号
140502 [人工智能];
摘要
In this work, we extend a common framework for graph-based image segmentation that includes the graph cuts, random walker, and shortest path optimization algorithms. Viewing an image as a weighted graph, these algorithms can be expressed by means of a common energy function 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 spanning forest algorithm for watershed in this same framework. 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 watershed algorithm, which we term the power watershed. In particular, when q = 2, the power watershed leads to a multilabel, scale and contrast invariant, unique global optimum obtained in practice in quasi-linear time. Placing the watershed algorithm in this energy minimization framework also opens new possibilities for using unary terms in traditional watershed segmentation and using watershed to optimize more general models of use in applications beyond image segmentation.
引用
收藏
页码:1384 / 1399
页数:16
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