COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Object modelling by registration of multiple range images
Image and Vision Computing - Special issue: range image understanding
Rapid octree construction from image sequences
CVGIP: Image Understanding
A volumetric method for building complex models from range images
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
The digital Michelangelo project: 3D scanning of large statues
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour Continuity in Region Based Image Segmentation
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Automatic 3D Model Construction for Turn-Table Sequences
SMILE'98 Proceedings of the European Workshop on 3D Structure from Multiple Images of Large-Scale Environments
3D shape recovery of complex objects from multiple silhouette images
Pattern Recognition Letters
Automatically Labeling Video Data Using Multi-class Active Learning
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM SIGGRAPH 2004 Papers
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric Context from a Single Image
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Photo tourism: exploring photo collections in 3D
ACM SIGGRAPH 2006 Papers
Cosegmentation of Image Pairs by Histogram Matching - Incorporating a Global Constraint into MRFs
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Surface Capture for Performance-Based Animation
IEEE Computer Graphics and Applications
Articulated mesh animation from multi-view silhouettes
ACM SIGGRAPH 2008 papers
Measuring uncertainty in graph cut solutions
Computer Vision and Image Understanding
Towards Scalable Dataset Construction: An Active Learning Approach
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
GeoS: Geodesic Image Segmentation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Co-segmentation of image pairs with quadratic global constraint in MRFs
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Cosegmentation revisited: models and optimization
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Inducing semantic segmentation from an example
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Shape-from-Silhouette with two mirrors and an uncalibrated camera
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
iModel: interactive co-segmentation for object of interest 3d modeling
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part II
Active learning for interactive segmentation with expected confidence change
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Object co-segmentation via discriminative low rank matrix recovery
Proceedings of the 21st ACM international conference on Multimedia
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We present an algorithm for Interactive Co-segmentation of a foreground object from a group of related images. While previous works in co-segmentation have focussed on unsupervised co-segmentation, we use successful ideas from the interactive object-cutout literature. We develop an algorithm that allows users to decide what foreground is, and then guide the output of the co-segmentation algorithm towards it via scribbles. Interestingly, keeping a user in the loop leads to simpler and highly parallelizable energy functions, allowing us to work with significantly more images per group. However, unlike the interactive single-image counterpart, a user cannot be expected to exhaustively examine all cutouts (from tens of images) returned by the system to make corrections. Hence, we propose iCoseg, an automatic recommendation system that intelligently recommends where the user should scribble next. We introduce and make publicly available the largest co-segmentation dataset yet, the CMU-Cornell iCoseg dataset, with 38 groups, 643 images, and pixelwise hand-annotated groundtruth. Through machine experiments and real user studies with our developed interface, we show that iCoseg can intelligently recommend regions to scribble on, and users following these recommendations can achieve good quality cutouts with significantly lower time and effort than exhaustively examining all cutouts.