Active learning for structured probabilistic models with histogram approximation

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn 07-12-June-2015, Issue , 2015, Pages 3612-3621

  Sun, Qing ^a   Laddha, Ankit ^b   Batra, Dhruv ^a  

^a VIRGINIA POLYTECHNIC INSTITUTE AND STATE UNIVERSITY   (United States)

^b CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER VISION; GRAPHIC METHODS; IMAGE SEGMENTATION;

ACTIVE LEARNING; CONDITIONAL RANDOM FIELDS(CRFS); GIBBS DISTRIBUTION; MULTI-CLASS CLASSIFICATION; PREDICTION PROBLEM; PROBABILISTIC MODELS; STRUCTURED PREDICTION;

PATTERN RECOGNITION;

EID: 84959206846     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2015.7298984     Document Type: Conference Paper

References (65)

1. J. Hays and A. A. Efros, "im2gps: estimating geographic information from a single image," in CVPR, 2008
2. D. Parikh and C. Zitnick, "The role of features, algorithms and data in visual recognition," in CVPR, 2010
3. X. Zhu, C. Vondrick, D. Ramanan, and C. Fowlkes, "Do we need more training data or better models for object detection," in BMVC, 2012
4. M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, "The pascal visual object classes (voc) challenge," IJCV, vol. 88, pp. 303-338, June 2010
5. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, "ImageNet: A Large-Scale Hierarchical Image Database," in CVPR, 2009
6. T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, "Microsoft COCO: Common objects in context," 2014
7. B. Settles, Active Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan & Claypool, 2012
8. M. J. Wainwright and M. I. Jordan, "Graphical models, exponential families, and variational inference," Foundations and Trends in Machine Learning, vol. 1, no. 1-2, pp. 1-305, 2008
9. L. G. Valiant, "The complexity of computing the permanent," Theoretical Computer Science, vol. 8, no. 2, 1979
10. D. Batra, P. Yadollahpour, A. Guzman-Rivera, and G. Shakhnarovich, "Diverse M-Best Solutions in Markov Random Fields," in ECCV, 2012
11. F. Meier, A. Globerson, and F. Sha, "The More the Merrier: Parameter Learning for Graphical Models with Multiple MAPs," in ICML Workshop on Inferning: Interactions between Inference and Learning, 2013
12. A. Prasad, S. Jegelka, and D. Batra, "Submodular meets structured: Finding diverse subsets in exponentially-large structured item sets," in NIPS, 2014
13. D. Hoiem, A. A. Efros, and M. Hebert, "Recovering surface layout from an image," IJCV, vol. 75, no. 1, 2007
14. B. C. Russell, A. Torralba, K. P. Murphy, and W. T. Freeman, "LabelMe: A database and web-based tool for image annotation," IJCV, vol. 77, pp. 157-173, May 2008
15. L. von Ahn and L. Dabbish, "Labeling images with a computer game," in CHI, CHI '04, 2004
16. J. Winn and N. Jojic, "LOCUS: learning object classes with unsupervised segmentation," in CVPR, 2005
17. H. Arora, N. Loeff, D. Forsyth, and N. Ahuja, "Unsupervised segmentation of objects using efficient learning," in CVPR, 2007
18. J. Xu, A. G. Schwing, and R. Urtasun, "Tell me what you see and I will show you where it is," in CVPR, 2014
19. X. He and R. S. Zemel, "Learning hybrid models for image annotation with partially labeled data.," in NIPS, 2008
20. J. Verbeek and W. Triggs, "Scene Segmentation with CRFs Learned from Partially Labeled Images," in NIPS, 2008
21. Y. Boykov and M.-P. Jolly, "Interactive graph cuts for optimal boundary and region segmentation of objects in n-d images," ICCV, 2001
22. C. Rother, V. Kolmogorov, and A. Blake, ""GrabCut": interactive foreground extraction using iterated graph cuts," SIGGRAPH, 2004
23. D. Batra, R. Sukthankar, and T. Chen, "Semi-supervised clustering via learnt codeword distances," in BMVC, 2008
24. D. Batra, A. Kowdle, D. Parikh, J. Luo, and T. Chen, "iCoseg: Interactive Co-segmentation with Intelligent Scribble Guidance," in CVPR, 2010
25. D. Küttel, M. Guillaumin, and V. Ferrari, "Segmentation propagation in imagenet," in ECCV, 2012
26. Y. Freund, H. Seung, E. Shamir, and N. Tishby, "Selective sampling using the query by committee algorithm," Machine Learning, vol. 28, no. 2-3, pp. 133-168, 1997
27. S. Tong and D. Koller, "Support vector machine active learning with applications to text classification," JMLR, vol. 2, pp. 45-66, Mar. 2002
28. D. MacKay, "Information-based objective functions for active data selection," Neural Computation, vol. 4, no. 4, pp. 590-604, 1992
29. G.-J. Qi, X.-S. Hua, Y. Rui, J. Tang, and H.-J. Zhang, "Two-dimensional active learning for image classification," in CVPR, pp. 1-8, 2008
30. A. Kapoor, K. Grauman, R. Urtasun, and T. Darrell, "Active learning with Gaussian processes for object categorization," in ICCV, 2007
31. P. Jain and A. Kapoor, "Active learning for large multi-class problems," in CVPR, 2009
32. R. Yan, J. Yang, and A. Hauptmann, "Automatically labeling video data using multi-class active learning," in ICCV, 2003
33. B. Collins, J. Deng, K. Li, and L. Fei-Fei, "Towards scalable dataset construction: An active learning approach," in ECCV, 2008
34. A. Fathi, M. F. Balcan, X. Ren, and J. M. Rehg, "Combining self training and active learning for video segmentation," in BMVC, 2011. http://dx. doi. org/10. 5244/C. 25. 78
35. S. Vijayanarasimhan and K. Grauman, "Active frame selection for label propagation in videos," in ECCV, 2012
36. C. Vondrick, D. Patterson, and D. Ramanan, "Efficiently scaling up crowdsourced video annotation," IJCV, vol. 101, pp. 184-204, Jan. 2013
37. B. Settles and M. Craven, "An analysis of active learning strategies for sequence labeling tasks," in EMNLP, 2008
38. A. Culotta and A. McCallum, "Reducing labeling effort for structured prediction tasks," in AAAI, 2005
39. W. Luo, A. G. Schwing, and R. Urtasun, "Latent Structured Active Learning," in NIPS, 2013
40. T. Hazan and A. Shashua, "Norm-product belief propagation: Primal-dual message-passing for approximate inference," Information Theory, IEEE Trans. on, vol. 56, pp. 6294-6316, Dec 2010
41. S. Maji, T. Hazan, and T. Jaakkola, "Active boundary annotation using random map perturbations," in AISTATS, 2014
42. G. Papandreou and A. L. Yuille, "Perturb-and-map random fields: Using discrete optimization to learn and sample from energy models," in ICCV, 2011
43. D. Tarlow, R. P. Adams, and R. S. Zemel, "Randomized optimum models for structured prediction," in Fifteenth Inter-national Conference on Artificial Intelligence and Statistics (AISTATS), 2012
44. S. Vijayanarasimhan and K. Grauman, "What's it going to cost you?: Predicting effort vs. informativeness for multilabel image annotations," in CVPR, 2009
45. B. Siddiquie and A. Gupta, "Beyond active noun tagging: Modeling contextual interactions for multi-class active learning.," in CVPR, 2010
46. A. Parkash and D. Parikh, "Attributes for classifier feedback," in ECCV, 2012
47. D. Koller and N. Friedman, Probabilistic Graphical Models: Principles and Techniques. MIT Press, 2009
48. D. Nilsson, "An efficient algorithm for finding the m most probable configurations in probabilistic expert systems," Statistics and Computing, vol. 8, pp. 159-173, 1998. 10. 1023/A:1008990218483
49. C. Yanover and Y. Weiss, "Finding the m most probable configurations using loopy belief propagation," in NIPS, 2003
50. D. Batra, "An Efficient Message-Passing Algorithm for the M-Best MAP Problem," in Uncertainty in Artificial Intelli-gence, 2012
51. C. Chen, V. Kolmogorov, Y. Zhu, D. Metaxas, and C. H. Lampert, "Computing the m most probable modes of a graphical model," in AISTATS, 2013
52. M. Schmidt, "http://www. cs. ubc. ca/~schmidtm/Software/UGM. html. " UGM: A Matlab toolbox for probabilistic undirected graphical models
53. I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun, "Large margin methods for structured and interdependent output variables," JMLR, vol. 6, pp. 1453-1484, 2005
54. T. Joachims, T. Finley, and C.-N. Yu, "Cutting-plane training of structural svms," Machine Learning, vol. 77, no. 1, pp. 27-59, 2009
55. A. Guzman-Rivera, P. Kohli, and D. Batra, "Divmcuts: Faster training of structural svms with diverse m-best cutting-planes.," in AISTATS, 2013
56. J. C. Platt, "Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods," in Ad-vances in Large Margin Classifiers, 1999
57. A. Niculescu-Mizil and R. Caruana, "Predicting good probabilities with supervised learning," in ICML, 2005
58. P. Kohli and P. H. S. Torr, "Measuring uncertainty in graph cut solutions," CVIU, vol. 112, no. 1, pp. 30-38, 2008
59. D. Roth and K. Small, "Margin-based active learning for structured output spaces," in ECML, 2006
60. A. Guzman-Rivera, D. Batra, and P. Kohli, "Multiple Choice Learning: Learning to Produce Multiple Structured Outputs," in Proc. NIPS, 2012
61. A. Guzman-Rivera, P. Kohli, D. Batra, and R. Rutenbar, "Efficiently enforcing diversity in multi-output structured prediction," in AISTATS, 2014
62. R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk, "SLIC superpixels compared to state-of-the-art superpixel methods," PAMI, vol. 34, no. 11, 2012
63. Y. Boykov, O. Veksler, and R. Zabih, "Efficient approximate energy minimization via graph cuts," PAMI, vol. 20, no. 12, pp. 1222-1239, 2001
64. V. Kolmogorov and R. Zabih, "What energy functions can be minimized via graph cuts," PAMI, vol. 26, no. 2, pp. 147-159, 2004
65. A. Sorokin and D. Forsyth, "Utility data annotation with amazon mechanical turk," in Workshop on Internet Vision, CVPR., pp. 1-8, 2008.