Directed hypergraphs and applications
Discrete Applied Mathematics - Special issue: combinatorial structures and algorithms
Intelligent scissors for image composition
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
International Journal of Computer Vision
Combinatorics and image processing
Graphical Models and Image Processing
Multilevel hypergraph partitioning: applications in VLSI domain
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Flux Maximizing Geometric Flows
IEEE Transactions on Pattern Analysis and Machine Intelligence
Directed Hypergraphs: Data Structures and Applications
CAAP '88 Proceedings of the 13th Colloquium on Trees in Algebra and Programming
Application of Adaptive Hypergraph Model to Impulsive Noise Detection
CAIP '01 Proceedings of the 9th International Conference on Computer Analysis of Images and Patterns
Learning a Classification Model for Segmentation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Iterative-improvement-based declustering heuristics for multi-disk databases
Information Systems
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
Random Walks for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Parallel and Distributed Systems
Learning, detection and representation of multi-agent events in videos
Artificial Intelligence
Efficient Label Propagation for Interactive Image Segmentation
ICMLA '07 Proceedings of the Sixth International Conference on Machine Learning and Applications
Unsupervised segmentation of natural images via lossy data compression
Computer Vision and Image Understanding
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Image Segmentation as Learning on Hypergraphs
ICMLA '08 Proceedings of the 2008 Seventh International Conference on Machine Learning and Applications
Geodesic Matting: A Framework for Fast Interactive Image and Video Segmentation and Matting
International Journal of Computer Vision
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
Interactive image segmentation using probabilistic hypergraphs
Pattern Recognition
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
Power Watershed: A Unifying Graph-Based Optimization Framework
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hypergraph-Based image representation
GbRPR'05 Proceedings of the 5th IAPR international conference on Graph-Based Representations in Pattern Recognition
Fast cost-volume filtering for visual correspondence and beyond
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Computer Science Review
STIA'12 Proceedings of the Second international conference on Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data
Directed-hypergraph Based Personalized E-learning Process and Resource Optimization
ICDH '12 Proceedings of the 2012 Fourth International Conference on Digital Home
Hi-index | 0.00 |
In this paper, we introduce for the first time the notion of directed hypergraphs in image processing and particularly image segmentation. We give a formulation of a random walk in a directed hypergraph that serves as a basis to a semi-supervised image segmentation procedure that is configured as a machine learning problem, where a few sample pixels are used to estimate the labels of the unlabeled ones. A directed hypergraph model is proposed to represent the image content, and the directed random walk formulation allows to compute a transition matrix that can be exploited in a simple iterative semi-supervised segmentation process. Experiments over the Microsoft GrabCut dataset have achieved results that demonstrated the relevance of introducing directionality in hypergraphs for computer vision problems.