Algorithms for clustering data
Algorithms for clustering data
Exact sampling with coupled Markov chains and applications to statistical mechanics
Proceedings of the seventh international conference on Random structures and algorithms
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mixing properties of the Swendsen-Wang process on classes of graphs
Random Structures & Algorithms - Special issue on statistical physics methods in discrete probability, combinatorics, and theoretical computer science
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Segmentation by Data-Driven Markov Chain Monte Carlo
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pairwise Data Clustering by Deterministic Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
A bounding chain for Swendsen-Wang
Random Structures & Algorithms
Parsing Images into Region and Curve Processes
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
What Energy Functions Can Be Minimized via Graph Cuts?
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
A Maximum-Flow Formulation of the N-Camera Stereo Correspondence Problem
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Graph Partition by Swendsen-Wang Cuts
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Comparison of Graph Cuts with Belief Propagation for Stereo, using Identical MRF Parameters
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Multigrid and multi-level Swendsen-Wang cuts for hierarchic graph partition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Minimizing Nonsubmodular Functions with Graph Cuts-A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recovering Surface Layout from an Image
International Journal of Computer Vision
A stochastic grammar of images
Foundations and Trends® in Computer Graphics and Vision
Learning and Inferring Motion Patterns using Parametric Segmental Switching Linear Dynamic Systems
International Journal of Computer Vision
Stereo Matching Using Population-Based MCMC
International Journal of Computer Vision
Cue Integration for Urban Area Extraction in Remote Sensing Images
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Regional category parsing in undirected graphical models
Pattern Recognition Letters
Bayesian image recovery for dendritic structures under low signal-to-noise conditions
IEEE Transactions on Image Processing
A Hierarchical and Contextual Model for Aerial Image Parsing
International Journal of Computer Vision
A variational framework for adaptive satellite images segmentation
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
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
Bayesian inference for layer representation with mixed Markov random field
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
From a set of shapes to object discovery
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Embedding Gestalt laws on conditional random field for image segmentation
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part I
Computer Vision and Image Understanding
A bayesian approach for building detection in densely build-up high resolution satellite image
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
Statistical priors for efficient combinatorial optimization via graph cuts
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Structured Learning and Prediction in Computer Vision
Foundations and Trends® in Computer Graphics and Vision
Centralized and distributed task allocation in multi-robot teams via a stochastic clustering auction
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Learning domain knowledge for façade labelling
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Diverse M-best solutions in markov random fields
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Window annealing for pixel-labeling problems
Computer Vision and Image Understanding
Importance sampling for parametric estimation
Proceedings of the Winter Simulation Conference
An Efficient Stochastic Clustering Auction for Heterogeneous Robotic Collaborative Teams
Journal of Intelligent and Robotic Systems
Probabilistic Joint Image Segmentation and Labeling by Figure-Ground Composition
International Journal of Computer Vision
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Many vision tasks can be formulated as graph partition problems that minimize energy functions. For such problems, the Gibbs sampler [9] provides a general solution but is very slow, while other methods, such as Ncut [24] and graph cuts [4], [22], are computationally effective but only work for specific energy forms [17] and are not generally applicable. In this paper, we present a new inference algorithm that generalizes the Swendsen-Wang method [25] to arbitrary probabilities defined on graph partitions. We begin by computing graph edge weights, based on local image features. Then, the algorithm iterates two steps. 1) Graph clustering: It forms connected components by cutting the edges probabilistically based on their weights. 2) Graph relabeling: It selects one connected component and flips probabilistically, the coloring of all vertices in the component simultaneously. Thus, it realizes the split, merge, and regrouping of a "chunk驴 of the graph, in contrast to Gibbs sampler that flips a single vertex. We prove that this algorithm simulates ergodic and reversible Markov chain jumps in the space of graph partitions and is applicable to arbitrary posterior probabilities or energy functions defined on graphs. We demonstrate the algorithm on two typical problems in computer vision驴image segmentation and stereo vision. Experimentally, we show that it is 100-400 times faster in CPU time than the classical Gibbs sampler and 20-40 times faster then the DDMCMC segmentation algorithm [27]. For stereo, we compare performance with graph cuts and belief propagation. We also show that our algorithm can automatically infer generative models and obtain satisfactory results (better than the graphic cuts or belief propagation) in the same amount of time.