Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
An Alternate Objective Function for Markovian Fields
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Bayesian Network Modeling of Hangul Characters for On-line Handwriting Recognition
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Contextual Recognition of Hand-Drawn Diagrams with Conditional Random Fields
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Learning to Parse Hierarchical Lists and Outlines Using Conditional Random Fields
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
A Generic Approach for On-Line Handwriting Recognition
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
ChemInk: a natural real-time recognition system for chemical drawings
Proceedings of the 16th international conference on Intelligent user interfaces
Located hidden random fields: learning discriminative parts for object detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Hi-index | 0.00 |
Many diagrams contain compound objects composed of parts.We propose a recognition framework that learns parts in an unsupervised way, and requires training labels only for compound objects. Thus, human labeling effort is reduced and parts are not predetermined, instead appropriate parts are discovered based on the data. We model contextual relations between parts, such that the label of a part can depend simultaneously on the labels of its neighbors, as well as spatial and temporal information. The model is a Hidden Random Field (HRF), an extension of a Conditional Random Field. We apply it to find parts of boxes, arrows and flowchart shapes in hand-drawn diagrams, and also demonstrate improved recognition accuracy over the conditional random field model without parts.