Higher order Markov networks for model estimation

Authors:
Toufiq Parag;Ahmed Elgammal
Affiliations:
Dept. of Computer Science, Rutgers University, Piscataway, NJ;Dept. of Computer Science, Rutgers University, Piscataway, NJ
Venue:
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part I
Year:
2011

Citing 8
Cited 0

Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography

Communications of the ACM
Saliency, Scale and Image Description

International Journal of Computer Vision
Energy Minimization via Graph Cuts: Settling What is Possible

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Linear Programming Approach to Max-Sum Problem: A Review

IEEE Transactions on Pattern Analysis and Machine Intelligence
Minimization of Locally Defined Submodular Functions by Optimal Soft Arc Consistency

Constraints
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning

Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
Efficient belief propagation with learned higher-order markov random fields

ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Factor graphs and the sum-product algorithm

IEEE Transactions on Information Theory

Quantified Score

Hi-index	0.00

Visualization

Abstract

The problem we address in this paper is to label datapoints when the information about them is provided primarily in terms of their subsets or groups. The knowledge we have for a group is a numerical weight or likelihood value for each group member to belong to same class. These likelihood values are computed given a class specific model, either explicit or implicit, of the pattern we wish to learn. By defining a Conditional Random Field (CRF) over the labels of data, we formulate the problem as an Markov Network inference problem. We present experimental results for analytical model estimation and object localization where the proposed method produces improved performances over other methods.