Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Learning and relearning in Boltzmann machines
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
A revolution: belief propagation in graphs with cycles
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
A tutorial on learning with Bayesian networks
Learning in graphical models
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Transformation-Invariant Clustering Using the EM Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Expectation Propagation for approximate Bayesian inference
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Transformed Component Analysis: Joint Estimation of Spatial Transformations and Image Components
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
An Introduction to Variational Methods for Graphical Models
An Introduction to Variational Methods for Graphical Models
Epitomic analysis of appearance and shape
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Extending factor graphs so as to unify directed and undirected graphical models
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
On the optimality of solutions of the max-product belief-propagation algorithm in arbitrary graphs
IEEE Transactions on Information Theory
Program verification as probabilistic inference
Proceedings of the 34th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages
On the computational rationale for generative models
Computer Vision and Image Understanding
Event detection using "variable module graphs" for home care applications
EURASIP Journal on Applied Signal Processing
International Journal of Computer Vision
Semantic image classification using statistical local spatial relations model
Multimedia Tools and Applications
Efficient MRF deformation model for non-rigid image matching
Computer Vision and Image Understanding
Efficiently Learning Random Fields for Stereo Vision with Sparse Message Passing
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Iterated conditional modes for inverse dithering
Signal Processing
Bayesian separation of images modeled with MRFs using MCMC
IEEE Transactions on Image Processing
A variational inference framework for soft-in soft-out detection in multiple-access channels
IEEE Transactions on Information Theory
A Study of Parts-Based Object Class Detection Using Complete Graphs
International Journal of Computer Vision
Learning natural scene categories by selective multi-scale feature extraction
Image and Vision Computing
Robust least-squares image matching in the presence of outliers
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
A variational bayes approach to image segmentation
BVAI'07 Proceedings of the 2nd international conference on Advances in brain, vision and artificial intelligence
International Journal of Sensor Networks
Multifactor expectation maximization for factor graphs
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Hierarchical Bayesian language models for conversational speech recognition
IEEE Transactions on Audio, Speech, and Language Processing
Registration of longitudinal image sequences with implicit template and spatial-temporal heuristics
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
A transductive multi-label learning approach for video concept detection
Pattern Recognition
Reconstruction of 4D-CT from a single free-breathing 3D-CT by spatial-temporal image registration
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
A comparison on score spaces for expression microarray data classification
PRIB'11 Proceedings of the 6th IAPR international conference on Pattern recognition in bioinformatics
Learning of graphical models and efficient inference for object class recognition
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Visual focus of attention recognition in the ambient kitchen
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
On Learning Conditional Random Fields for Stereo
International Journal of Computer Vision
Analysis of Markov Boundary Induction in Bayesian Networks: A New View From Matroid Theory
Fundamenta Informaticae
The lazy flipper: efficient depth-limited exhaustive search in discrete graphical models
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
Recognizing occluded faces by exploiting psychophysically inspired similarity maps
Pattern Recognition Letters
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Research into methods for reasoning under uncertainty is currently one of the most exciting areas of artificial intelligence, largely because it has recently become possible to record, store, and process large amounts of data. While impressive achievements have been made in pattern classification problems such as handwritten character recognition, face detection, speaker identification, and prediction of gene function, it is even more exciting that researchers are on the verge of introducing systems that can perform large-scale combinatorial analyses of data, decomposing the data into interacting components. For example, computational methods for automatic scene analysis are now emerging in the computer vision community. These methods decompose an input image into its constituent objects, lighting conditions, motion patterns, etc. Two of the main challenges are finding effective representations and models in specific applications and finding efficient algorithms for inference and learning in these models. In this paper, we advocate the use of graph-based probability models and their associated inference and learning algorithms. We review exact techniques and various approximate, computationally efficient techniques, including iterated conditional modes, the expectation maximization (EM) algorithm, Gibbs sampling, the mean field method, variational techniques, structured variational techniques and the sum-product algorithm ("loopy驴 belief propagation). We describe how each technique can be applied in a vision model of multiple, occluding objects and contrast the behaviors and performances of the techniques using a unifying cost function, free energy.