Learning from neighboring strokes: Combining appearance and context for multi-domain sketch recognition

Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference

Volumn , Issue , 2009, Pages 1401-1409

  Ouyang, Tom Y ^a   Davis, Randall ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DRAWING STYLES; ELECTRICAL CIRCUIT; FREEHAND SKETCH; GRAPHICAL MODEL; JOINT MODELS; MOLECULAR DIAGRAMS; MULTI-DOMAINS; REAL WORLD DOMAIN; SKETCH RECOGNITION; VISUAL APPEARANCE;

EID: 79952743100     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (22)

1. C. Alvarado and R. Davis. Sketchread: A multi-domain sketch recognition engine. In Proc. ACM Symposium on User Interface Software and Technology, 2004.
2. M. Everingham, L. Van Gool, C. Williams, J. Winn, and A. Zisserman. The pascal visual object classes challenge 2008 results, 2008.
3. L. Gennari, L. Kara, T. Stahovich, and K. Shimada. Combining geometry and domain knowledge to interpret hand-drawn diagrams. Computers & Graphics, 29(4):547-562, 2005.
4. M. Gross. The electronic cocktail napkina computational environment for working with design diagrams. Design Studies, 17(1):53-69, 1996.
5. T. Hammond and R. Davis. Ladder: a language to describe drawing, display, and editing in sketch recognition. In Proc. International Conference on Computer Graphics and Interactive Techniques, 2006.
6. X. He, R. Zemel, and M. Carreira-Perpinan. Multiscale conditional random fields for image labeling. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2004.
7. L. Kara and T. Stahovich. An image-based, trainable symbol recognizer for hand-drawn sketches. Computers & Graphics, 29(4):501-517, 2005.
8. K. Murphy, A. Torralba, and W. Freeman. Using the forest to see the trees: a graphical model relating features, objects and scenes. Advances in Neural Information Processing Systems, 2003.
9. M. Oltmans. Envisioning Sketch Recognition: A Local Feature Based Approach to Recognizing Informal Sketches. PhD thesis, Massachusetts Institute of Technology, Cambridge, MA, May 2007.
10. T. Y. Ouyang and R. Davis. Recognition of hand drawn chemical diagrams. In Proc. AAAI Conference on Artificial Intelligence, 2007.
11. T. Y. Ouyang and R. Davis. A visual approach to sketched symbol recognition. In Proc. International Joint Conferences on Artificial Intelligence, 2009.
12. J. Platt. Probabilities for sv machines. Advances in Neural Information Processing Systems, 1999.
13. J. Platt. Sequential minimal optimization: A fast algorithm for training support vector machines. Advances in Kernel Methods-Support Vector Learning, 1999.
14. X. Ren and J. Malik. Learning a classification model for segmentation. In Proc. IEEE International Conference on Computer Vision, pages 10-17, 2003.
15. T. Sezgin and R. Davis. Sketch based interfaces: Early processing for sketch understanding. In Proc. International Conference on Computer Graphics and Interactive Techniques. ACM New York, NY, USA, 2006.
16. T. Sezgin and R. Davis. Sketch recognition in interspersed drawings using time-based graphical models. Computers & Graphics, 32(5):500-510, 2008.
17. M. Shilman, H. Pasula, S. Russell, and R. Newton. Statistical visual language models for ink parsing. Proc. AAAI Spring Symposium on Sketch Understanding, 2002.
18. M. Shilman, P. Viola, and K. Chellapilla. Recognition and grouping of handwritten text in diagrams and equations. In Proc. International Workshop on Frontiers in Handwriting Recognition, 2004.
19. J. Shotton, J. Winn, C. Rother, and A. Criminisi. Textonboost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation. Lecture Notes in Computer Science, 3951:1, 2006.
20. M. Szummer. Learning diagram parts with hidden random fields. In Proc. International Conference on Document Analysis and Recognition, 2005.
21. M. Tappen and W. Freeman. Comparison of graph cuts with belief propagation for stereo, using identical mrf parameters. In Proc. IEEE International Conference on Computer Vision, 2003.
22. J. Yedidia, W. Freeman, and Y. Weiss. Understanding belief propagation and its generalizations. Exploring Artificial Intelligence in the New Millennium, pages 239-269, 2003.