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AN APPROACH TOWARD FAST GRADIENT-BASED IMAGE SEGMENTATION
ABSTRACT
We present and investigate an approach to fast multi label color image segmentation
usingconvex optimization techniques. The presented model is in someways related to the well-
known Mumford–Shah model, butdeviates in certain important aspects. The optimization
problemhas been designed with two goals in mind. The objective functionshould represent
fundamental concepts of image segmentation,such as incorporation of weighted curve length and
variationof intensity in the segmented regions, while allowing transformationinto a convex
concave saddle point problem that iscomputationally inexpensive to solve. This paper
introducessuch a model, the nontrivial transformation of this model intoa convex–concave saddle
point problem, and the numericaltreatment of the problem. We evaluate our approach by
applyingour algorithm to various images and show that our resultsare competitive in terms of
quality at unprecedentedly lowcomputation times. Our algorithm allows high-quality
segmentationof megapixel images in a few seconds and achieves interactiveperformance for low
resolution.

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An approach toward fast gradient based image segmentation

  • 1. AN APPROACH TOWARD FAST GRADIENT-BASED IMAGE SEGMENTATION ABSTRACT We present and investigate an approach to fast multi label color image segmentation usingconvex optimization techniques. The presented model is in someways related to the well- known Mumford–Shah model, butdeviates in certain important aspects. The optimization problemhas been designed with two goals in mind. The objective functionshould represent fundamental concepts of image segmentation,such as incorporation of weighted curve length and variationof intensity in the segmented regions, while allowing transformationinto a convex concave saddle point problem that iscomputationally inexpensive to solve. This paper introducessuch a model, the nontrivial transformation of this model intoa convex–concave saddle point problem, and the numericaltreatment of the problem. We evaluate our approach by applyingour algorithm to various images and show that our resultsare competitive in terms of quality at unprecedentedly lowcomputation times. Our algorithm allows high-quality segmentationof megapixel images in a few seconds and achieves interactiveperformance for low resolution.