Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Intelligent scissors for image composition
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Combinatorics and image processing
Graphical Models and Image Processing
Normalized Cuts and Image Segmentation
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
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Geometric Level Set Methods in Imaging,Vision,and Graphics
Geometric Level Set Methods in Imaging,Vision,and Graphics
Learning a Classification Model for Segmentation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Semi-Supervised Learning on Riemannian Manifolds
Machine Learning
Multi.Objective Hypergraph Partitioning Algorithms for Cut and Maximum Subdomain Degree Minimization
Proceedings of the 2003 IEEE/ACM international conference on Computer-aided design
Semi-supervised learning using randomized mincuts
ICML '04 Proceedings of the twenty-first international conference on Machine learning
"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
Gaussian Markov Random Fields: Theory And Applications (Monographs on Statistics and Applied Probability)
Higher order learning with graphs
ICML '06 Proceedings of the 23rd international conference on Machine learning
Learning high-order MRF priors of color images
ICML '06 Proceedings of the 23rd international conference on Machine learning
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
Using Multiple Segmentations to Discover Objects and their Extent in Image Collections
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Random Walks for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Learning from interpretations: a rooted kernel for ordered hypergraphs
Proceedings of the 24th international conference on Machine learning
Graph Laplacians and their Convergence on Random Neighborhood Graphs
The Journal of Machine Learning Research
Image Segmentation as Learning on Hypergraphs
ICMLA '08 Proceedings of the 2008 Seventh International Conference on Machine Learning and Applications
Sampling strategies for bag-of-features image classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Hypergraph-Based image representation
GbRPR'05 Proceedings of the 5th IAPR international conference on Graph-Based Representations in Pattern Recognition
Efficient belief propagation with learned higher-order markov random fields
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Enhancing interactive image segmentation with automatic label set augmentation
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Random walks on graphs for salient object detection in images
IEEE Transactions on Image Processing
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
Interactive segmentation of non-star-shaped contours by dynamic programming
Pattern Recognition
An image segmentation method for Chinese paintings by combining deformable models with graph cuts
HCII'11 Proceedings of the 14th international conference on Human-computer interaction: design and development approaches - Volume Part I
Random walks in directed hypergraphs and application to semi-supervised image segmentation
Computer Vision and Image Understanding
Hi-index | 0.01 |
This paper introduces a novel interactive framework for segmenting images using probabilistic hypergraphs which model the spatial and appearance relations among image pixels. The probabilistic hypergraph provides us a means to pose image segmentation as a machine learning problem. In particular, we assume that a small set of pixels, which are referred to as seed pixels, are labeled as the object and background. The seed pixels are used to estimate the labels of the unlabeled pixels by learning on a hypergraph via minimizing a quadratic smoothness term formed by a hypergraph Laplacian matrix subject to the known label constraints. We derive a natural probabilistic interpretation of this smoothness term, and provide a detailed discussion on the relation of our method to other hypergraph and graph based learning methods. We also present a front-to-end image segmentation system based on the proposed method, which is shown to achieve promising quantitative and qualitative results on the commonly used GrabCut dataset.