Active learning for sketch recognition

Computers and Graphics (Pergamon)

Volumn 52, Issue , 2015, Pages 93-105

  Yanik, Erelcan ^a   Sezgin, Tevfik Metin ^a  

^a KOÇ UNIVERSITY   (Turkey)

Author keywords

Active learning; ANOVA; Empirical analysis; Factor analysis; Sketch recognition

Indexed keywords

ANALYSIS OF VARIANCE (ANOVA); COMPUTER GRAPHICS; FACTOR ANALYSIS;

ACTIVE LEARNING; ANNOTATED TRAINING DATA; COMPUTER GRAPHICS APPLICATIONS; EMPIRICAL ANALYSIS; FEATURE REPRESENTATION; NATURAL INTERACTIONS; PEN-BASED INTERFACES; SKETCH RECOGNITION;

ARTIFICIAL INTELLIGENCE;

EID: 84940417374     PISSN: 00978493     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cag.2015.07.023     Document Type: Article

References (34)

1. Lee W, de Silva R, Peterson EJ, Calfee RC, Stahovich TF. Newton's pen: a pen-based tutoring system for statics. Comput Graph 2008;32(5):511-24.
2. Taele P, Hammond T. Lamps: a sketch recognition-based teaching tool for mandarin phonetic symbols i. J Vis Lang Comput 2010;21(2):109-20.
3. Shesh A, Chen B. Smartpaper: an interactive and user friendly sketching system. Comput Graph Forum 2004;23(3):301-10.
4. Juchmes R, Leclercq P, Azar S. A freehand-sketch environment for architectural design supported by a multi-agent system. Comput Graph 2005;29(6):905-15.
5. Hammond T, Davis R. Ladder, a sketching language for user interface developers. Comput Graph 2005;29(4):518-32.
6. Hou S, Ramani K. Classifier combination for sketch-based 3d part retrieval. Comput Graph 2007;31(4):598-609.
7. Alvarado C, Davis R. Resolving ambiguities to create a natural computer-based sketching environment. In: Proceedings of the 17th international joint conference on artificial intelligence. Seattle, Washington, USA: IJCAI; 2001. p. 1365-74.
8. Fu L, Kara LB. Neural network-based symbol recognition using a few labeled samples. Comput Graph 2011;35(5):955-66.
9. Kaster BL, Jacobson ER, Hammond TA. Sssousa: automatically generating secure and searchable data collection studies. In: International workshop on visual languages and computing. Redwood City, CA, USA: VLC; 2009. p. 365-8.
10. Oltmans M, Alvarado C, Davis R. Etcha sketches: lessons learned from collecting sketch data. In: Making pen-based interaction intelligent and natural. Menlo Park, CA, USA: AAAI; 2004. p. 134-40.
11. Blagojevic R, Plimmer B, Grundy J, Wang Y. A data collection tool for sketched diagrams. In: Proceedings of the 5th eurographics conference on sketch-based interfaces and modeling. Annecy, France: Eurographics Association; 2008. p. 73-80.
12. Wolin A, Smith D, Alvarado C. A pen-based tool for efficient labeling of 2d sketches. In: Proceedings of the 4th eurographics workshop on sketch-based interfaces and modeling. New York, NY, USA: ACM; 2007. p. 67-74.
13. Zhen J, Blagojevic R, Plimmer B. Automated labeling of ink stroke data. In: Proceedings of the international symposium on sketch-based interfaces and modeling. Aire-la-Ville, Switzerland: Eurographics Association; 2012. p. 67-75.
14. Settles B. From theories to queries: active learning in practice. In: Workshop on active learning and experimental design. Sardinia, Italy: JMLR; 2011. p. 1-18.
15. Schein AI, Ungar LH. Active learning for logistic regression: an evaluation. Mach Learn 2007;68(3):235-65.
16. Gasperin C. Active learning for anaphora resolution. In: Proceedings of the NAACL HLT 2009 workshop on active learning for natural language processing. Stroudsburg, PA, USA: Association for Computational Linguistics; 2009. p. 1-8.
17. Guo Y, Schuurmans D. Discriminative batch mode active learning. In: Platt J, Koller D, Singer Y, Roweis S, editors. Advances in neural information processing systems, vol. 20. Cambridge, MA: MIT Press; 2008. p. 593-600.
18. Settles B. Active learning literature survey. Computer Sciences Technical Report 1648, University of Wisconsin-Madison; 2010.
19. Settles B, Craven M. An analysis of active learning strategies for sequence labeling tasks. In: Proceedings of the conference on empirical methods in natural language processing. Stroudsburg, PA, USA: Association for Computational Linguistics; 2008. p. 1070-9.
20. Lewis DD, Gale WA. A sequential algorithm for training text classifiers. In: Proceedings of the 17th annual international ACM SIGIR conference on research and development in information retrieval. New York, NY, USA: Springer-Verlag New York, Inc.; 1994. p. 3-12.
21. Olsson F. A literature survey of active machine learning in the context of natural language processing. Technical Report T2009-06, Swedish Institute of Computer Science; 2009.
22. McCallum A, Nigam K. Employing EM and pool-based active learning for text classification. In: Proceedings of the 15th international conference on machine learning. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.; 1998. p. 350-8.
23. Melville P, Yang SM, Saar-Tsechansky M, Mooney R. Active learning for probability estimation using jensen-shannon divergence. In: Proceedings of the 16th european conference on machine learning. Berlin, Heidelberg: Springer-Verlag; 2005. p. 268-79.
24. Engelson SP, Dagan I. Minimizing manual annotation cost in supervised training from corpora. In: Proceedings of the 34th annual meeting of the association for computational linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics; 1996. p. 319-26.
25. Shen D, Zhang J, Su J, Zhou G, Tan CL. Multi-criteria-based active learning for named entity recognition. In: Proceedings of the 42nd annual meeting on association for computational linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics; 2004. p. 589-96.
26. Brinker K. Incorporating diversity in active learning with support vector machines. In: Fawcett T, Mishra N, editors. ICML. Washington, DC, USA: AAAI Press; 2003. p. 59-66.
27. Arandjelović R, Sezgin TM. Sketch recognition by fusion of temporal and image-based features. Pattern Recognit 2011;44:1225-34.
28. Niels R, Willems D, Vuurpijl L. The NicIcon database of handwritten icons. In: 11th International conference on the frontiers of handwriting recognition (ICFHR 2008). Montreal, Canada; 2008. p. 296-301.
29. Tumen RS, Acer ME, Sezgin TM. Feature extraction and classifier combination for image-based sketch recognition. In: Proceedings of the seventh sketch-based interfaces and modeling symposium. Aire-la-Ville, Switzerland, Switzerland: Eurographics Association; 2010. p. 63-70.
30. Khotanzad A, Hong Y. Invariant image recognition by zernike moments. IEEE Trans Pattern Anal Mach Intell 1990;12(5):489-97.
31. Ouyang TY, Davis R. A visual approach to sketched symbol recognition. In: Proceedings of the 21st international joint conference on artifical intelligence. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.; 2009. p. 1463-8.
32. Baram Y, El-Yaniv R, Luz K. Online choice of active learning algorithms. J Mach Learn Res 2004;5:255-91.
33. Markowitz TJ. An empirical evaluation of active learning and selective sampling variations supporting large corpus labeling, [PhD thesis], Pace University; 2011.
34. Sun S, Hardoon DR. Active learning with extremely sparse labeled examples. Neurocomputing 2010;73(16-18):2980-8.