Putting the Scientist in the Loop - Accelerating Scientific Progress with Interactive Machine Learning

Proceedings - International Conference on Pattern Recognition

Volumn , Issue , 2014, Pages 9-17

  Aodha, Oisin Mac ^a   Stathopoulos, Vassilios ^e   Brostow, Gabriel J ^b   Terry, Michael ^c,d   Girolami, Mark ^a   Jones, Kate E ^e  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

^b UNIVERSITY OF WATERLOO   (Canada)

^c UNIVERSITY COLLEGE LONDON   (United Kingdom)

^d INSTITUTE OF ZOOLOGY   (United Kingdom)

^e UNIVERSITY OF WARWICK   (United Kingdom)

Author keywords

Biodiversity; Computer vision; Data visualization; Ecology; Human computer interaction; Interactive machine learning

Indexed keywords

BIODIVERSITY; COMPUTER VISION; DATA ACQUISITION; DATA HANDLING; DATA VISUALIZATION; DIGITAL STORAGE; ECOLOGY; HUMAN COMPUTER INTERACTION; KNOWLEDGE MANAGEMENT; PATTERN RECOGNITION; PIPELINES;

CROSS-DISCIPLINARY; DATA COLLECTION; DATA LABELING; INTERACTIVE MACHINE LEARNING; LEVELS OF DETAIL; SCIENTIFIC PROGRESS; STANDARD MACHINES;

MACHINE LEARNING;

EID: 84919933195     PISSN: 10514651     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICPR.2014.12     Document Type: Conference Paper

References (75)

1. G. M. Mace, K. Norris, and A. H. Fitter, "Biodiversity and ecosystem services: A multilayered relationship," Trends in ecology &evolution, 2012.
2. I. J. Bateman, A. R. Harwood, G. M. Mace, R. T. Watson, D. J. Abson, B. Andrews, A. Binner, A. Crowe, B. H. Day, S. Dugdale et al., "Bringing ecosystem services into economic decision-making: land use in the united kingdom," science, 2013.
3. S. H. Butchart, M. Walpole, B. Collen, A. van Strien, J. P. Scharlemann, R. E. Almond, J. E. Baillie, B. Bomhard, C. Brown, J. Bruno et al., "Global biodiversity: indicators of recent declines," Science, 2010.
4. C. Bellard, C. Bertelsmeier, P. Leadley, W. Thuiller, and F. Courchamp, "Impacts of climate change on the future of biodiversity," Ecology letters, 2012.
5. J. M. Rowcliffe and C. Carbone, "Surveys using camera traps: are we looking to a brighter future?" Animal Conservation, 2008.
6. K. E. Jones, J. A. Russ, A.-T. Bashta, Z. Bilhari, C. Catto, I. Csosz, A. Gorbachev, P. Gyorfi, A. Hughes, I. Ivashkiv et al., "Indicator bats program: A system for the global acoustic monitoring of bats," Biodiversity Monitoring and Conservation: Bridging the Gap between Global Commitment and Local Action, 2013.
7. "Bat Detective," 2012. [Online]. Available: www.batdetective.org
8. "Snapshot Serengeti," 2012. [Online]. Available: www.snapshotserengeti.org
9. J. S. Kim, M. J. Greene, A. Zlateski, K. Lee, M. Richardson, S. C. Turaga, M. Purcaro, M. Balkam, A. Robinson, B. F. Behabadi, M. Campos, W. Denk, H. S. Seung, and the EyeWirers, "Space-time wiring specificity supports direction selectivity in the retina," Nature, 2014.
10. T. Desell, R. Bergman, K. Goehner, R. Marsh, R. VanderClute, and S. Ellis-Felege, "Wildlife@ home: Combining crowd sourcing and volunteer computing to analyze avian nesting video," in International Conference on eScience, 2013.
11. C. Vondrick, D. Patterson, and D. Ramanan, "Efficiently scaling up crowdsourced video annotation," IJCV, 2013.
12. A. Fielding, Machine learning methods for ecological applications. Springer, 1999.
13. F. Recknagel, "Applications of machine learning to ecological modelling," Ecological Modelling, 2001.
14. C. L. Walters, R. Freeman, A. Collen, C. Dietz, M. Brock Fenton, G. Jones, M. K. Obrist, S. J. Puechmaille, T. Sattler, B. M. Siemers et al., "A continental-scale tool for acoustic identification of european bats," Journal of Applied Ecology, 2012.
15. K. Wagstaff, "Machine learning that matters," in ICML, 2012.
16. A. Torralba and A. A. Efros, "Unbiased look at dataset bias," in CVPR, 2011.
17. J. A. Fails and D. R. Olsen Jr, "Interactive machine learning," in International Conference on Intelligent User Interfaces, 2003.
18. T. Kulesza, S. Amershi, R. Caruana, D. Fisher, and D. Charles, "Structured labeling for facilitating concept evolution in machine learning," in CHI, 2014.
19. "ZSL Instant Wild," 2011. [Online]. Available: www.edgeofexistence.org/instantwild
20. T. S. Brandes, "Automated sound recording and analysis techniques for bird surveys and conservation," Bird Conservation International, 2008.
21. C. L. Walters, A. Collen, T. Lucas, K. Mroz, C. A. Sayer, and K. E. Jones, "Challenges of using bioacoustics to globally monitor bats," in Bat Evolution, Ecology, and Conservation, 2013.
22. X. P. Burgos-Artizzu, P. Dollár, D. Lin, D. J. Anderson, and P. Perona, "Social behavior recognition in continuous video," in CVPR, 2012.
23. J. Sivic and A. Zisserman, "Video google: A text retrieval approach to object matching in videos," in CVPR, 2003.
24. D. G. Lowe, "Distinctive image features from scale-invariant keypoints," IJCV, 2004.
25. S. Lazebnik, C. Schmid, and J. Ponce, "Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories," in CVPR, 2006.
26. F. Perronnin, J. Sánchez, and T. Mensink, "Improving the fisher kernel for large-scale image classification," in ECCV, 2010.
27. D. Oneata, J. Verbeek, and C. Schmid, "Action and event recognition with fisher vectors on a compact feature set," in ICCV, 2013.
28. N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," in CVPR, 2005.
29. S. Davis and P. Mermelstein, "Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences," Acoustics, Speech and Signal Processing, 1980.
30. V. Stathopoulos, V. Zamora-Gutierrez, K. Jones, and M. Girolami, "Bat call identification with gaussian process multinomial probit regression and a dynamic time warping kernel," AIStats, 2014.
31. K. Fukushima, "Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position," Biological cybernetics, 1980.
32. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," Proceedings of the IEEE, 1998.
33. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks." in NIPS, 2012.
34. G. E. Dahl, D. Yu, L. Deng, and A. Acero, "Context-dependent pretrained deep neural networks for large-vocabulary speech recognition," Audio, Speech, and Language Processing, 2012.
35. M. Kabra, A. A. Robie, M. Rivera-Alba, S. Branson, and K. Branson, "JAABA: interactive machine learning for automatic annotation of animal behavior," Nature Methods, 2013.
36. B. C. Russell, A. Torralba, K. P. Murphy, and W. T. Freeman, "Labelme: A database and web-based tool for image annotation," IJCV, 2008.
37. A. Yao, J. Gall, C. Leistner, and L. Van Gool, "Interactive object detection," in CVPR, 2012.
38. G. J. Brostow, J. Fauqueur, and R. Cipolla, "Semantic object classes in video: A high-definition ground truth database," Pattern Recognition Letters, 2009.
39. C. Vondrick and D. Ramanan, "Video annotation and tracking with active learning." in NIPS, 2011.
40. S. Bandla and K. Grauman, "Active learning of an action detector from untrimmed videos," in ICCV, 2013.
41. "Amazon Mechanical Turk," 2014. [Online]. Available: www.mturk.com
42. P. Welinder, S. Branson, S. Belongie, and P. Perona, "The multidimensional wisdom of crowds." in NIPS, 2010.
43. V. C. Raykar, S. Yu, L. H. Zhao, A. Jerebko, C. Florin, G. H. Valadez, L. Bogoni, and L. Moy, "Supervised learning from multiple experts: whom to trust when everyone lies a bit," in ICML, 2009.
44. B. Settles, Active learning. Morgan &Claypool, 2012.
45. O. Mac Aodha, N. D. Campbell, J. Kautz, and G. J. Brostow, "Hierarchical subquery evaluation for active learning on a graph," CVPR, 2014.
46. A. Vezhnevets, J. M. Buhmann, and V. Ferrari, "Active learning for semantic segmentation with expected change," in CVPR, 2012.
47. S. Vijayanarasimhan and K. Grauman, "Large-scale live active learning: Training object detectors with crawled data and crowds," in CVPR, 2011.
48. V. C. Raykar, S. Yu, L. H. Zhao, G. H. Valadez, C. Florin, L. Bogoni, and L. Moy, "Learning from crowds," JMLR, 2010.
49. P. G. Ipeirotis, F. Provost, and J. Wang, "Quality management on amazon mechanical turk," in Proceedings of the ACM SIGKDD Workshop on Human Computation, 2010.
50. C. Cortes and V. Vapnik, "Support-vector networks," Machine learning, 1995.
51. L. Breiman, "Random forests," Machine learning, 2001.
52. C. E. Rasmussen and C. K. I. Williams, Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning). The MIT Press, 2005.
53. M. Neal, Radford, "Probabilistic inference using Markov chain Monte Carlo methods," Dept. of Computer Science, University of Toronto, Tech. Rep. CRG-TR-93-1, 1993.
54. M. Girolami and B. Calderhead, "Riemann manifold langevin and hamiltonian monte carlo methods," Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2011.
55. T. P. Minka, "Expectation propagation for approximate bayesian inference," in Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence, 2001.
56. H. Attias, "A variational bayesian framework for graphical models," in NIPS, 2000.
57. Stan Development Team, "Stan: A c++ library for probability and sampling, version 2.2," 2014. [Online]. Available: http://mc-stan.org/
58. T. Minka, J. Winn, J. Guiver, and D. Knowles, "Infer.NET 2.5," 2012, Microsoft Research Cambridge. http://research.microsoft.com/infernet.
59. L. M. Surhone, M. T. Tennoe, and S. F. Henssonow, OpenBUGS. Mauritius: Betascript Publishing, 2010.
60. J. Kruschke, Doing Bayesian data analysis: A tutorial introduction with R. Academic Press, 2010.
61. A. Asuncion and D. Newman, "Uci machine learning repository," 2007.
62. X. Zhu, C. Vondrick, D. Ramanan, and C. Fowlkes, "Do we need more training data or better models for object detection?." in BMVC, 2012.
63. A. Lapedriza, H. Pirsiavash, Z. Bylinskii, and A. Torralba, "Are all training examples equally valuable?" arXiv preprint arXiv:1311.6510, 2013.
64. S. Vijayanarasimhan and K. Grauman, "Cost-sensitive active visual category learning," IJCV, 2011.
65. P. Vatturi and W.-K. Wong, "Category detection using hierarchical mean shift," in KDD, 2009.
66. T. S. Haines and T. Xiang, "Active rare class discovery and classification using dirichlet processes," IJCV, 2014.
67. S. J. Pan and Q. Yang, "A survey on transfer learning," Knowledge and Data Engineering, 2010.
68. O. Mac Aodha, A. Humayun, M. Pollefeys, and G. J. Brostow, "Learning a confidence measure for optical flow," PAMI, 2013.
69. M. Reynolds, J. Dobos, L. Peel, T. Weyrich, and G. J. Brostow, "Capturing time-of-flight data with confidence," in CVPR, 2011.
70. O. Mac Aodha and G. J. Brostow, "Revisiting Example Dependent Cost-Sensitive Learning with Decision Trees," in ICCV, 2013.
71. C. García Cifuentes, M. Sturzel, F. Jurie, and G. J. Brostow, "Motion models that only work sometimes," in BMVC, 2012.
72. J. Talbot, B. Lee, A. Kapoor, and D. S. Tan, "Ensemblematrix: interactive visualization to support machine learning with multiple classifiers," in CHI, 2009.
73. T. May, A. Bannach, J. Davey, T. Ruppert, and J. Kohlhammer, "Guiding feature subset selection with an interactive visualization," in Visual Analytics Science and Technology, 2011.
74. L. Van der Maaten and G. Hinton, "Visualizing Data using t-SNE," JMLR, 2008.
75. C. Nguyen, Y. Niu, and F. Liu, "Video summagator: an interface for video summarization and navigation," in CHI, 2012.