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Image Segmentation with A Bounding Box Prior
"... Userprovided object bounding box is a simple and popular interaction paradigm considered by many existing interactive image segmentation frameworks. However, these frameworks tend to exploit the provided bounding box merely to exclude its exterior from consideration and sometimes to initialize the ..."
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Cited by 36 (3 self)
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Userprovided object bounding box is a simple and popular interaction paradigm considered by many existing interactive image segmentation frameworks. However, these frameworks tend to exploit the provided bounding box merely to exclude its exterior from consideration and sometimes to initialize the energy minimization. In this paper, we discuss how the bounding box can be further used to impose a powerful topological prior, which prevents the solution from excessive shrinking and ensures that the userprovided box bounds the segmentation in a sufficiently tight way. The prior is expressed using hard constraints incorporated into the global energy minimization framework leading to an NPhard integer program. We then investigate the possible optimization strategies including linear relaxation as well as a new graph cut algorithm called pinpointing. The latter can be used either as a rounding method for the fractional LP solution, which is provably better than thresholdingbased rounding, or as a fast standalone heuristic. We evaluate the proposed algorithms on a publicly available dataset, and demonstrate the practical benefits of the new prior both qualitatively and quantitatively. 1.
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, 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 pa ..."
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Cited by 17 (7 self)
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In this work, we extend a common framework for seeded 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 watersheds 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 power watersheds. Placing the watershed algorithm in this energy minimization framework also opens new possibilities for using unary terms in traditional watershed segmentation and using watersheds to optimize more general models of use in application beyond image segmentation. 1.
Globally optimal segmentation of multiregion objects
 In ICCV
, 2009
"... colours are hard to separate. In the absence of user localization, above at center is the best result we can expect from such models. Now we can design multiregion models with geometric interactions to segment such objects more robustly in a single graph cut. Many objects contain spatially distinct ..."
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Cited by 16 (2 self)
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colours are hard to separate. In the absence of user localization, above at center is the best result we can expect from such models. Now we can design multiregion models with geometric interactions to segment such objects more robustly in a single graph cut. Many objects contain spatially distinct regions, each with a unique colour/texture model. Mixture models ignore the spatial distribution of colours within an object, and thus cannot distinguish between coherent parts versus randomly distributed colours. We show how to encode geometric interactions between distinct region+boundary models, such as regions being interior/exterior to each other along with preferred distances between their boundaries. With a single graph cut, our method extracts only those multiregion objects that satisfy such a combined model. We show applications in medical segmentation and scene layout estimation. Unlike Li et al. [17] we do not need “domain unwrapping” nor do we have topological limits on shapes. 1.
High Resolution Matting via Interactive Trimap Segmentation Technical report corresponding to the CVPR’08 paper TR1882200804
"... We present a new approach to the matting problem which splits the task into two steps: interactive trimap extraction followed by trimapbased alpha matting. By doing so we gain considerably in terms of speed and quality and are able to deal with high resolution images. This paper has three contribut ..."
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Cited by 14 (5 self)
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We present a new approach to the matting problem which splits the task into two steps: interactive trimap extraction followed by trimapbased alpha matting. By doing so we gain considerably in terms of speed and quality and are able to deal with high resolution images. This paper has three contributions: (i) a new trimap segmentation method using parametric maxflow; (ii) an alpha matting technique for high resolution images with a new gradient preserving prior on alpha; (iii) a database of 27 ground truth alpha mattes of still objects, which is considerably larger than previous databases and also of higher quality. The database is used to train our system and to validate that both our trimap extraction and our matting method improve on stateoftheart techniques. 1.
Boundary Learning by Optimization with Topological Constraints Supplementary Material
"... In this section we provide additional details of the experimental comparisons that were performed in Section 4 of the main text. We also show an extended presentation of the warping error results shown in the main text. In particular Figure 1 shows the warping error on the test set of the convolutio ..."
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Cited by 12 (1 self)
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In this section we provide additional details of the experimental comparisons that were performed in Section 4 of the main text. We also show an extended presentation of the warping error results shown in the main text. In particular Figure 1 shows the warping error on the test set of the convolutional network methods along with BEL and gPbOWTUCM. For this comparison, a threshold of gPbOWTUCM and BEL was chosen according to the threshold that achieved lowest Rand error also on the test set (shown in Figure 4 of the main text). These results are consistent with the relative ordering of algorithms that the Rand index produced, but the relative reduction in error between the methods is larger (for example, the gPbOWTUCM method has almost ten times as much warping error as the highest performer, BLOTC CN). Figure 2 also shows a visual depiction of the segmentation and boundary maps of all methods that are discussed. 1.1. Multiscale Normalized Cut Multiscale normalized cut was performed using publicly available code provided by the authors of [1]:
Power Watershed: A Unifying GraphBased Optimization Framework
, 2011
"... In this work, we extend a common framework for graphbased 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 ..."
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Cited by 11 (3 self)
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In this work, we extend a common framework for graphbased 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 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 quasilinear 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.
Vertex sparsifiers: New results from old techniques
 IN 13TH INTERNATIONAL WORKSHOP ON APPROXIMATION, RANDOMIZATION, AND COMBINATORIAL OPTIMIZATION, VOLUME 6302 OF LECTURE NOTES IN COMPUTER SCIENCE
, 2010
"... Given a capacitated graph G = (V, E) and a set of terminals K ⊆ V, how should we produce a graph H only on the terminals K so that every (multicommodity) flow between the terminals in G could be supported in H with low congestion, and vice versa? (Such a graph H is called a flowsparsifier for G.) ..."
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Cited by 10 (4 self)
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Given a capacitated graph G = (V, E) and a set of terminals K ⊆ V, how should we produce a graph H only on the terminals K so that every (multicommodity) flow between the terminals in G could be supported in H with low congestion, and vice versa? (Such a graph H is called a flowsparsifier for G.) What if we want H to be a “simple ” graph? What if we allow H to be a convex combination of simple graphs? Improving on results of Moitra [FOCS 2009] and Leighton and Moitra [STOC 2010], we give efficient algorithms for constructing: (a) a flowsparsifier H that log k log log k maintains congestion up to a factor of O (), where k = K. (b) a convex combination of trees over the terminals K that maintains congestion up to a factor of O(log k). (c) for a planar graph G, a convex combination of planar graphs that maintains congestion up to a constant factor. This requires us to give a new algorithm for the 0extension problem, the first one in which the preimages of each terminal are connected in G. Moreover, this result extends to minorclosed families of graphs. Our bounds immediately imply improved approximation guarantees for several terminalbased cut and ordering problems.
Geodesic Star Convexity for Interactive Image Segmentation
"... In this paper we introduce a new shape constraint for interactive image segmentation. It is an extension of Veksler’s [25] starconvexity prior, in two ways: from a single star to multiple stars and from Euclidean rays to Geodesic paths. Global minima of the energy function are obtained subject to t ..."
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Cited by 10 (0 self)
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In this paper we introduce a new shape constraint for interactive image segmentation. It is an extension of Veksler’s [25] starconvexity prior, in two ways: from a single star to multiple stars and from Euclidean rays to Geodesic paths. Global minima of the energy function are obtained subject to these new constraints. We also introduce Geodesic Forests, which exploit the structure of shortest paths in implementing the extended constraints. The starconvexity prior is used here in an interactive setting and this is demonstrated in a practical system. The system is evaluated by means of a “robot user ” to measure the amount of interaction required in a precise way. We also introduce a new and harder dataset which augments the existing Grabcut dataset [1] with images and ground truth taken from the PASCAL VOC segmentation challenge [7]. 1.
Submodularity beyond submodular energies: coupling edges in graph cuts
 In CVPR
, 2011
"... We propose a new family of nonsubmodular global energy functions that still use submodularity internally to couple edges in a graph cut. We show it is possible to develop an efficient approximation algorithm that, thanks to the internal submodularity, can use standard graph cuts as a subroutine. We ..."
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Cited by 10 (8 self)
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We propose a new family of nonsubmodular global energy functions that still use submodularity internally to couple edges in a graph cut. We show it is possible to develop an efficient approximation algorithm that, thanks to the internal submodularity, can use standard graph cuts as a subroutine. We demonstrate the advantages of edge coupling in a natural setting, namely image segmentation. In particular, for finestructured objects and objects with shading variation, our structured edge coupling leads to significant improvements over standard approaches. 1.