Fast Trust Region for Segmentation

Gorelick, Lena; Schmidt, Frank; Boykov, Yuri

doi:10.1109/cvpr.2013.224

Cited by 30 publications

(72 citation statements)

References 18 publications

(43 reference statements)

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“…parameter. This practical consideration is important when selecting parametric pseudo-bound families for specific applications, e.g (12), (16), or (19). Note that parametric maxflow can be easily parallelized to further accelerate our algorithm.…”

Section: Motivation and Contributionsmentioning

confidence: 99%

“…Recently high-order [2,3,12,13,15,26,29,38] and non-submodular pairwise [11,16,21,19] energy minimization have drawn tremendous research interests. Those energy functions arise naturally in many computer vision and image processing applications.…”

Section: Introductionmentioning

confidence: 99%

“…The trust region size is adaptively adjusted depending on the quality of current approximation using well-known trust region paradigms [39]. The recent studies [11,12] have shown that FTR achieved the state of the art performance in many applications. Our work is more closely related to bound optimizers, which take auxiliary functions [2,3,11,22,27,29,31] as local approximations and were recently shown to yield competitive performances in several vision problems [3,11,29].…”

Section: Introductionmentioning

confidence: 99%

“…Those energy functions arise naturally in many computer vision and image processing applications. Examples of high-order functions include but are not limited to constraints on segment volume [12,38], clique labeling consistency [13,18,35] and matching target distributions [2,3,12,29]. Pairwise non-submodular energies occur in deconvolution [11], curvature regularization [8,11,28], inpainting [16] and surface registration [16].…”

Section: Introductionmentioning

confidence: 99%

“…By relaxing the integrality constraints, they globally transform the original function into a linear function with extra variables and linear constraints. Unlike global linearization, local techniques iteratively approximate the original energy around current solution, for instance, using Taylor approximations [11,12,17,24] or auxiliary functions [2,3,11,27,29,31]. The recent Fast Trust Region (FTR) method [12] finds the optimal solution of a local approximation within a trust region, i.e.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Pseudo-bound Optimization for Binary Energies

Tang

Ayed

Boykov

2014

Computer Vision – ECCV 2014

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Abstract. High-order and non-submodular pairwise energies are important for image segmentation, surface matching, deconvolution, tracking and other computer vision problems. Minimization of such energies is generally NP-hard. One standard approximation approach is to optimize an auxiliary function -an upper bound of the original energy across the entire solution space. This bound must be amenable to fast global solvers. Ideally, it should also closely approximate the original functional, but it is very difficult to find such upper bounds in practice.Our main idea is to relax the upper-bound condition for an auxiliary function and to replace it with a family of pseudo-bounds, which can better approximate the original energy. We use fast polynomial parametric maxflow approach to explore all global minima for our family of submodular pseudo-bounds. The best solution is guaranteed to decrease the original energy because the family includes at least one auxiliary function. Our Pseudo-Bound Cuts algorithm improves the state-of-the-art in many applications: appearance entropy minimization, target distribution matching, curvature regularization, image deconvolution and interactive segmentation.

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Section: Motivation and Contributionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Pseudo-bound Optimization for Binary Energies

Tang

Ayed

Boykov

2014

Computer Vision – ECCV 2014

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TRIC: Trust Region for Invariant Compactness and Its Application to Abdominal Aorta Segmentation

Ayed

Wang

Miles

et al. 2014

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014

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Convexity Shape Prior for Segmentation

Gorelick

Veksler

Boykov

et al. 2014

Computer Vision – ECCV 2014

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Abstract. Convexity is known as an important cue in human vision. We propose shape convexity as a new high-order regularization constraint for binary image segmentation. In the context of discrete optimization, object convexity is represented as a sum of 3-clique potentials penalizing any 1-0-1 configuration on all straight lines. We show that these non-submodular interactions can be efficiently optimized using a trust region approach. While the cubic number of all 3-cliques is prohibitively high, we designed a dynamic programming technique for evaluating and approximating these cliques in linear time. Our experiments demonstrate general usefulness of the proposed convexity constraint on synthetic and real image segmentation examples. Unlike standard secondorder length regularization, our convexity prior is scale invariant, does not have shrinking bias, and is virtually parameter-free.

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Fast Trust Region for Segmentation

Cited by 30 publications

References 18 publications

Pseudo-bound Optimization for Binary Energies

Pseudo-bound Optimization for Binary Energies

TRIC: Trust Region for Invariant Compactness and Its Application to Abdominal Aorta Segmentation

Convexity Shape Prior for Segmentation

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