Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Hierarchical Image Analysis Using Irregular Tessellations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Elements of information theory
Elements of information theory
1994 Special Issue: A fast dynamic link matching algorithm for invariant pattern recognition
Neural Networks - Special issue: models of neurodynamics and behavior
Introduction to Monte Carlo methods
Learning in graphical models
An Introduction to Variational Methods for Graphical Models
Machine Learning
Partially Occluded Object Recognition Using Statistical Models
International Journal of Computer Vision
Variational Methods for Multimodal Image Matching
International Journal of Computer Vision
Principal Manifolds and Probabilistic Subspaces for Visual Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Belief Networks and Neural Networks for Scene Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic Trees: A Structured Variational Method Giving Efficient Propagation Rules
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
A continuous probabilistic framework for image matching
Computer Vision and Image Understanding
Image Modeling with Position-Encoding Dynamic Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Distance Using Hidden Markov Models
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
Object Detection Using the Statistics of Parts
International Journal of Computer Vision
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
IEEE Transactions on Information Theory
An overlapping tree approach to multiscale stochastic modeling and estimation
IEEE Transactions on Image Processing
Discrete Markov image modeling and inference on the quadtree
IEEE Transactions on Image Processing
Multiscale methods for the segmentation and reconstruction of signals and images
IEEE Transactions on Image Processing
Sonar image segmentation using an unsupervised hierarchical MRF model
IEEE Transactions on Image Processing
Multiscale Bayesian segmentation using a trainable context model
IEEE Transactions on Image Processing
Multiscale image segmentation using wavelet-domain hidden Markov models
IEEE Transactions on Image Processing
A tree-structured Markov random field model for Bayesian image segmentation
IEEE Transactions on Image Processing
Interpretation of complex scenes using dynamic tree-structure Bayesian networks
Computer Vision and Image Understanding
Dynamic hierarchical Markov random fields and their application to web data extraction
Proceedings of the 24th international conference on Machine learning
Dynamic Hierarchical Markov Random Fields for Integrated Web Data Extraction
The Journal of Machine Learning Research
Dynamic trees for sensor fusion
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Hi-index | 0.14 |
We present a probabilistic framework驴namely, multiscale generative models known as Dynamic Trees (DT)驴for unsupervised image segmentation and subsequent matching of segmented regions in a given set of images. Beyond these novel applications of DTs, we propose important additions for this modeling paradigm. First, we introduce a novel DT architecture, where multilayered observable data are incorporated at all scales of the model. Second, we derive a novel probabilistic inference algorithm for DTs驴Structured Variational Approximation (SVA)驴which explicitly accounts for the statistical dependence of node positions and model structure in the approximate posterior distribution, thereby relaxing poorly justified independence assumptions in previous work. Finally, we propose a similarity measure for matching dynamic-tree models, representing segmented image regions, across images. Our results for several data sets show that DTs are capable of capturing important component-subcomponent relationships among objects and their parts, and that DTs perform well in segmenting images into plausible pixel clusters. We demonstrate the significantly improved properties of the SVA algorithm驴both in terms of substantially faster convergence rates and larger approximate posteriors for the inferred models驴when compared with competing inference algorithms. Furthermore, results on unsupervised object recognition demonstrate the viability of the proposed similarity measure for matching dynamic-structure statistical models.