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Erschienen in:
2018 | OriginalPaper | Buchkapitel
Multi-object Convexity Shape Prior for Segmentation
verfasst von : Lena Gorelick, Olga Veksler
Erschienen in: Energy Minimization Methods in Computer Vision and Pattern Recognition
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Abstract
Convexity is known as an important cue in human vision and has been recently proposed as a shape prior for segmenting a single foreground object. We propose a mutli-object convexity shape prior for multilabel image segmentation. We formulate a novel multilabel discrete energy function. To optimize our energy, we extend the trust region optimization framework recently proposed in the context of binary optimization. To that end we develop a novel graph construction. In addition to convexity constraints, our model includes \(L^1\) color separation term between the background and the foreground objects. It can also incorporate any other multilabel submodular energy term. Our formulation can be used to segment multiple convex objects sharing the same appearance model, or objects consisting of multiple convex parts. Our experiments demonstrate general usefulness of the proposed convexity constraint on real image segmentation examples.