Recursive segmentation and recognition templates for 2D parsing

Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference

Volumn , Issue , 2009, Pages 1985-1992

  Zhu, Long ^a   Chen, Yuanhao ^b   Lin, Yuan ^c   Lin, Chenxi ^d   Yuille, Alan ^e  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

^c SHANGHAI JIAO TONG UNIVERSITY   (China)

^d MICROSOFT RESEARCH ASIA   (China)

^e UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; FORMAL LANGUAGES; IMAGE SEGMENTATION; INFERENCE ENGINES; OBJECT RECOGNITION; POLYNOMIAL APPROXIMATION;

1-D STRUCTURES; 2D IMAGES; HIERARCHICAL IMAGE MODELING; HIERARCHICAL REPRESENTATION; IMAGE OBJECTS; IMAGE PARSING; NATURAL LANGUAGES; PARSING ALGORITHM; POLYNOMIAL-TIME; TWO DIMENSIONAL NATURE;

DYNAMIC PROGRAMMING;

EID: 84863377477     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (22)

1. F. Jelinek and J. D. Lafferty, "Computation of the probability of initial substring generation by stochastic context-free grammars," Computational Linguistics, vol. 17, no. 3, pp. 315-323, 1991.
2. M. Collins, "Head-driven statistical models for natural language parsing," Ph.D. Thesis, University of Pennsylvania, 1999.
3. K. Lari and S. J. Young, "The estimation of stochastic context-free grammars using the inside-outside algorithm," in Computer Speech and Languag, 1990.
4. M. Shilman, P. Liang, and P. A. Viola, "Learning non-generative grammatical models for document analysis," in Proceedings of IEEE International Conference on Computer Vision, 2005, pp. 962-969. (Pubitemid 44042285)
5. Z. Tu and S. C. Zhu, "Image segmentation by data-driven markov chain monte carlo," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 657-673, 2002. (Pubitemid 34550432)
6. Z. Tu, X. Chen, A. L. Yuille, and S. C. Zhu, "Image parsing: Unifying segmentation, detection, and recognition," in Proceedings of IEEE International Conference on Computer Vision, 2003, pp. 18-25.
7. J. D. Lafferty, A. McCallum, and F. C. N. Pereira, "Conditional random fields: Probabilistic models for segmenting and labeling sequence data," in Proceedings of International Conference on Machine Learning, 2001, pp. 282-289.
8. J. Shotton, J. M. Winn, C. Rother, and A. Criminisi, "TextonBoost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation," in Proceedings of European Conference on Computer Vision, 2006, pp. 1-15. (Pubitemid 44029764)
9. M. Collins, "Discriminative training methods for hidden markov models: theory and experiments with perceptron algorithms," in Proceedings of Annual Meeting on Association for Computational Linguistics conference on Empirical methods in natural language processing, 2002, pp. 1-8.
10. X. He, R. S. Zemel, and M. Á. Carreira-Perpinãń, "Multiscale conditional random fields for image labeling," in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004, pp. 695-702.
11. S. Kumar and M. Hebert, "A hierarchical field framework for unified context-based classification," in Proceedings of IEEE International Conference on Computer Vision, 2005, pp. 1284-1291. (Pubitemid 44042328)
12. E. L. Allwein, R. E. Schapire, and Y. Singer, "Reducing multiclass to binary: A unifying approach for margin classifiers," Journal of Machine Learning Research, vol. 1, pp. 113-141, 2000. (Pubitemid 33738778)
13. Y. Boykov and M.-P. Jolly, "Interactive graph cuts for optimal boundary and region segmentation of objects in n-d images," in Proceedings of IEEE International Conference on Computer Vision, 2001, pp. 105-112. (Pubitemid 32794944)
14. A. Oliva and A. Torralba, "Building the gist of a scene: the role of global image features in recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 155, pp. 23-36, 2006. (Pubitemid 44485189)
15. A. Levin and Y. Weiss, "Learning to combine bottom-up and top-down segmentation," in Proceedings of European Conference on Computer Vision, 2006, pp. 581-594. (Pubitemid 44029660)
16. E. B. Sudderth, A. B. Torralba, W. T. Freeman, and A. S. Willsky, "Learning hierarchical models of scenes, objects, and parts," in Proceedings of IEEE International Conference on Computer Vision, 2005, pp. 1331-1338. (Pubitemid 44042334)
17. Y. Chen, L. Zhu, C. Lin, A. L. Yuille, and H. Zhang, "Rapid inference on a novel and/or graph for object detection, segmentation and parsing," in Advances in Neural Information Processing Systems, 2007.
18. B. Taskar, D. Klein, M. Collins, D. Koller, and C. Manning, "Max-margin parsing," in Proceedings of Annual Meeting on Association for Computational Linguistics conference on Empirical methods in natural language processing, 2004.
19. L. Zhu, Y. Chen, X. Ye, and A. L. Yuille, "Structure-perceptron learning of a hierarchical log-linear model," in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2008.
20. J. Verbeek and B. Triggs, "Region classification with markov field aspect models," in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2007.
21. Z. Tu, "Auto-context and its application to high-level vision tasks," in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2008.
22. J. Verbeek and B. Triggs, "Scene segmentation with crfs learned from partially labeled images," in Advances in Neural Information Processing Systems, vol. 20, 2008.