International Journal of Computer Vision
Analyzing visual layout for a non-visual presentation-document interface
Proceedings of the 8th international ACM SIGACCESS conference on Computers and accessibility
Multi-domain sketch understanding
ACM SIGGRAPH 2007 courses
SBIM '07 Proceedings of the 4th Eurographics workshop on Sketch-based interfaces and modeling
A Simple and Efficient Model Pruning Method for Conditional Random Fields
ICCPOL '09 Proceedings of the 22nd International Conference on Computer Processing of Oriental Languages. Language Technology for the Knowledge-based Economy
A new gaussian mixture conditional random field model for indoor image labeling
IMCE '09 Proceedings of the 1st international workshop on Interactive multimedia for consumer electronics
Sketch recognition by fusion of temporal and image-based features
Pattern Recognition
Constellation models for sketch recognition
SBM'06 Proceedings of the Third Eurographics conference on Sketch-Based Interfaces and Modeling
Interest of syntactic knowledge for on-line flowchart recognition
GREC'11 Proceedings of the 9th international conference on Graphics Recognition: new trends and challenges
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Hand-drawn diagrams present a complex recognition problem. Elements of the diagram are often individually ambiguous, and require context to be interpreted. We present a recognition method based on Bayesian conditional random fields (BCRFs) that jointly analyzes all drawing elements in order to incorporate contextual cues. The classification of each object affects the classification of its neighbors. BCRFs allow flexible and correlated features, and take both spatial and temporal information into account. BCRFs estimate the posterior distribution of parameters during training, and average predictions over the posterior for testing. As a result of model averaging, BCRFs avoid the overfitting problems associated with maximum likelihood training. We also incorporate Automatic Relevance Determination (ARD), a Bayesian feature selection technique, into BCRFs. The result is significantly lower error rates compared to ML- and MAP-trained CRFs.