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307
Constrained Parametric MinCuts for Automatic Object Segmentation
, 2010
"... We present a novel framework for generating and rankingplausibleobjectshypothesesin animage using bottomup processes and midlevel cues. The object hypotheses arerepresented as figureground segmentations, and are extracted automatically, withoutpriorknowledgeabout properties of individual object c ..."
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Cited by 123 (11 self)
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We present a novel framework for generating and rankingplausibleobjectshypothesesin animage using bottomup processes and midlevel cues. The object hypotheses arerepresented as figureground segmentations, and are extracted automatically, withoutpriorknowledgeabout properties of individual object classes, by solving a sequence of constrained parametric mincut problems (CPMC) on a regular image grid. We then learn to rank the object hypotheses by training a continuous model to predict how plausible the segments are, given their midlevel region properties. We show that this algorithm significantly outperforms the state of the art for lowlevel segmentation in the VOC09 segmentation dataset. It achieves the same average best segmentation covering as the best performing technique to date [2], 0.61 when using just the top 7 ranked segments, instead of the full hierarchy in [2]. Our methodachieves0.78averagebest covering using 154 segments. In a companion paper [18], we also show that the algorithm achieves stateofthe art results when used in a segmentationbased recognition pipeline.
A Seeded Image Segmentation Framework Unifying Graph Cuts And Random Walker Which Yields A New Algorithm
 ICCV
, 2007
"... In this work, we present a common framework for seeded image segmentation algorithms that yields two of the leading methods as special cases The Graph Cuts and the Random Walker algorithms. The formulation of this common framework naturally suggests a new, third, algorithm that we develop here. Spe ..."
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Cited by 97 (9 self)
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In this work, we present a common framework for seeded image segmentation algorithms that yields two of the leading methods as special cases The Graph Cuts and the Random Walker algorithms. The formulation of this common framework naturally suggests a new, third, algorithm that we develop here. Specifically, the former algorithms may be shown to minimize a certain energy with respect to either an ℓ1 or an ℓ2 norm. Here, we explore the segmentation algorithm defined by an ℓ ∞ norm, provide a method for the optimization and show that the resulting algorithm produces an accurate segmentation that demonstrates greater stability with respect to the number of seeds employed than either the Graph Cuts or Random Walker methods.
Shapebased recognition of 3d point clouds in urban environments
 ICCV
, 2009
"... This paper investigates the design of a system for recognizing objects in 3D point clouds of urban environments. The system is decomposed into four steps: locating, segmenting, characterizing, and classifying clusters of 3D points. Specifically, we first cluster nearby points to form a set of potent ..."
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Cited by 64 (7 self)
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This paper investigates the design of a system for recognizing objects in 3D point clouds of urban environments. The system is decomposed into four steps: locating, segmenting, characterizing, and classifying clusters of 3D points. Specifically, we first cluster nearby points to form a set of potential object locations (with hierarchical clustering). Then, we segment points near those locations into foreground and background sets (with a graphcut algorithm). Next, we build a feature vector for each point cluster (based on both its shape and its context). Finally, we label the feature vectors using a classifier trained on a set of manually labeled objects. The paper presents several alternative methods for each step. We quantitatively evaluate the system and tradeoffs of different alternatives in a truthed part of a scan of Ottawa that contains approximately 100 million points and 1000 objects of interest. Then, we use this truth data as a training set to recognize objects amidst approximately 1 billion points of the remainder of the Ottawa scan. 1.
Active graph cuts
 In CVPR
, 2006
"... This paper adds a number of novel concepts into global s/t cut methods improving their efficiency and making them relevant for a wider class of applications in vision where algorithms should ideally run in realtime. Our new Active Cuts (AC) method can effectively use a good approximate solution (in ..."
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Cited by 50 (3 self)
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This paper adds a number of novel concepts into global s/t cut methods improving their efficiency and making them relevant for a wider class of applications in vision where algorithms should ideally run in realtime. Our new Active Cuts (AC) method can effectively use a good approximate solution (initial cut) that is often available in dynamic, hierarchical, and multilabel optimization problems in vision. In many problems AC works faster than the stateoftheart maxflow methods [2] even if initial cut is far from the optimal one. Moreover, empirical speed improves several folds when initial cut is spatially close to the optima. Before converging to a global minima, Active Cuts outputs a multitude of intermediate solutions (intermediate cuts) that, for example, can be used be accelerate iterative learningbased methods or to improve visual perception of graph cuts realtime performance when large volumetric data is segmented. Finally, it can also be combined with many previous methods for accelerating graph cuts. 1. Introduction and Related
Superpixels and supervoxels in an energy optimization framework
 In ECCV
, 2010
"... Abstract. Many methods for object recognition, segmentation, etc., rely on tessellation of an image into “superpixels”. A superpixel is an image patch which is better aligned with intensity edges than a rectangular patch. Superpixels can be extracted with any segmentation algorithm, however, most of ..."
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Cited by 44 (2 self)
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Abstract. Many methods for object recognition, segmentation, etc., rely on tessellation of an image into “superpixels”. A superpixel is an image patch which is better aligned with intensity edges than a rectangular patch. Superpixels can be extracted with any segmentation algorithm, however, most of them produce highly irregular superpixels, with widely varying sizes and shapes. A more regular space tessellation may be desired. We formulate the superpixel partitioning problem in an energy minimization framework, and optimize with graph cuts. Our energy function explicitly encourages regular superpixels. We explore variations of the basic energy, which allow a tradeoff between a less regular tessellation but more accurate boundaries or better efficiency. Our advantage over previous work is computational efficiency, principled optimization, and applicability to 3D “supervoxel ” segmentation. We achieve high boundary recall on 2D images and spatial coherence on video. We also show that compact superpixels improve accuracy on a simple application of salient object segmentation. Key words: Superpixels, supervoxels, graph cuts 1
A scalable graphcut algorithm for nd grids
 In Proceedings of CVPR
, 2008
"... Global optimisation via st graph cuts is widely used in computer vision and graphics. To obtain highresolution output, graph cut methods must construct massive ND gridgraphs containing billions of vertices. We show that when these graphs do not fit into physical memory, current maxflow/mincut ..."
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Cited by 41 (0 self)
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Global optimisation via st graph cuts is widely used in computer vision and graphics. To obtain highresolution output, graph cut methods must construct massive ND gridgraphs containing billions of vertices. We show that when these graphs do not fit into physical memory, current maxflow/mincut algorithms—the workhorse of graph cut methods—are totally impractical. Others have resorted to banded or hierarchical approximation methods that get trapped in local minima, which loses the main benefit of global optimisation. We enhance the pushrelabel algorithm for maximum flow [14] with two practical contributions. First, true global minima can now be computed on immense gridlike graphs too large for physical memory. These graphs are ubiquitous in computer vision, medical imaging and graphics. Second, for commodity multicore platforms our algorithm attains nearlinear speedup with respect to number of processors. To achieve these goals, we generalised the standard relabeling operations associated with pushrelabel. 1.
Shape Prior Segmentation of Multiple Objects with Graph Cuts
"... We present a new shape prior segmentation method using graph cuts capable of segmenting multiple objects. The shape prior energy is based on a shape distance popular with level set approaches. We also present a multiphase graph cut framework to simultaneously segment multiple, possibly overlapping o ..."
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Cited by 39 (2 self)
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We present a new shape prior segmentation method using graph cuts capable of segmenting multiple objects. The shape prior energy is based on a shape distance popular with level set approaches. We also present a multiphase graph cut framework to simultaneously segment multiple, possibly overlapping objects. The multiphase formulation differs from multiway cuts in that the former can account for object overlaps by allowing a pixel to have multiple labels. We then extend the shape prior energy to encompass multiple shape priors. Unlike variational methods, a major advantage of our approach is that the segmentation energy is minimized directly without having to compute its gradient, which can be a cumbersome task and often relies on approximations. Experiments demonstrate that our algorithm can cope with image noise and clutter, as well as partial occlusions and affine transformations of the shape. 1.
Segmentation by transduction
, 2008
"... This paper addresses the problem of segmenting an image into regions consistent with usersupplied seeds (e.g., a sparse set of broad brush strokes). We view this task as a statistical transductive inference, in which some pixels are already associated with given zones and the remaining ones need to ..."
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Cited by 37 (2 self)
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This paper addresses the problem of segmenting an image into regions consistent with usersupplied seeds (e.g., a sparse set of broad brush strokes). We view this task as a statistical transductive inference, in which some pixels are already associated with given zones and the remaining ones need to be classified. Our method relies on the Laplacian graph regularizer, a powerful manifold learning tool that is based on the estimation of variants of the LaplaceBeltrami operator and is tightly related to diffusion processes. Segmentation is modeled as the task of finding matting coefficients for unclassified pixels given known matting coefficients for seed pixels. The proposed algorithm essentially relies on a high margin assumption in the space of pixel characteristics. It is simple, fast, and accurate, as demonstrated by qualitative results on natural images and a quantitative comparison with stateoftheart methods on the Microsoft GrabCut segmentation database.
RepFinder: Finding Approximately Repeated Scene Elements for Image Editing
"... Figure 1: Repeated element detection and manipulation. (Lefttoright) Original image with user scribbles to indicate an object template (red) and background (green); repeated instances detected, completed, dense correspondence established, and ordered in layers; fish in the original image replaced ..."
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Cited by 35 (20 self)
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Figure 1: Repeated element detection and manipulation. (Lefttoright) Original image with user scribbles to indicate an object template (red) and background (green); repeated instances detected, completed, dense correspondence established, and ordered in layers; fish in the original image replaced by a different kind of fish from a reference image (topright inset); rearranged fishes. Repeated elements are ubiquitous and abundant in both manmade and natural scenes. Editing such images while preserving the repetitions and their relations is nontrivial due to overlap, missing parts, deformation across instances, illumination variation, etc. Manually enforcing such relations is laborious and errorprone. We propose a novel framework where user scribbles are used to guide detection and extraction of such repeated elements. Our detection process, which is based on a novel boundary band method, robustly extracts the repetitions along with their deformations. The algorithm only considers the shape of the elements, and ignores similarity based on color, texture, etc. We then use topological sorting to establish a partial depth ordering of overlapping repeated instances. Missing parts on occluded instances are completed using information from other instances. The extracted repeated instances can then be seamlessly edited and manipulated for a variety of high level tasks that are otherwise difficult to perform. We demonstrate the versatility of our framework on a large set of inputs of varying complexity, showing applications to image rearrangement, edit transfer, deformation propagation, and instance replacement. image editing, shapeaware manipulation, edit propaKeywords: gation