Toward interactive search in remote sensing imagery

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 7709, Issue , 2010, Pages

  Porter, Reid ^a   Hush, Don ^a   Harvey, Neal ^a   Theiler, James ^a  

^a LOS ALAMOS NATIONAL LABORATORY   (United States)

Author keywords

anomaly detection; change detection; interactive machine learning; Rare category detection

Indexed keywords

ANOMALY DETECTION; CHANGE DETECTION; HUMAN USERS; HUMAN-IN-THE-LOOP; INFORMATION PRODUCTS; INTERACTIVE SEARCH; MACHINE LEARNING PROBLEM; MACHINE-LEARNING; NEW DESIGN; RARE CATEGORY DETECTION; REAL-WORLD DATASETS; REMOTE SENSING IMAGERY; VISUAL ANALYTICS;

LEARNING SYSTEMS; REMOTE SENSING; SIGNAL DETECTION; VISUALIZATION;

SECURITY OF DATA;

EID: 79953689615     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.850787     Document Type: Conference Paper

References (14)

1. D. Pelleg, and A. Moore, "Active Learning for Anomaly and Rare-Category Detection." Proc. 18th Annual Conference on Neural Information Processing Systems, (2004).
2. C. R. Aragon, S. S. Poon, G. S. Aldering et al., "Using visual analytics to maintain situation awareness in astrophysics." Visual Analytics Science and Technology, 2008. VAST '08. IEEE Symposium on, 27-34, (2008).
3. W. A. Pike, J. Stasko, R. Chang et al., "The science of interaction," Information Visualization, 8(4), 263-274 (2009).
4. S. Fine, and Y. Mansour, "Active sampling for multiple output identification," Machine Learning, 69(2-3), 213-228 (2007).
5. P. Vatturi, and W.-K. Wong, "Category detection using hierarchical mean shift." Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, Paris, France, 847-856, (2009).
6. J. He, and J. Carbonell, "Nearest-Neighbor-Based Active Learning for Rare Category Detection." NIPS: Neural Information Processing Systems, Vancouver, B.C., Canada (2007).
7. J. He, L. Yan, and R. Lawrence, "Graph-based rare category detection." Proceedings - IEEE International Conference on Data Mining, ICDM, 833-838, (2008).
8. J. W. Stokes, J. C. Platt, J. Kravis et al., [ALADIN: Active Learning of Anomalies to Detect Intrusions] Microsoft Research, MSR-TR-2008-24, (2008).
9. N. Abe, B. Zadrozny, and J. Langford, "Outlier detection by active learning." Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, Philadelphia, PA, USA, 504-509, (2006).
10. T. Suzuki, M. Sugiyama, J. Sese et al., "Approximating Mutual Information by Maximum Likelihood Density Ratio Estimation." JMLR: Workshop and Conference Proceedings 4, 5-20, (2008).
11. I. Steinwart, D. Hush, and C. Scovel, "A classification framework for anomaly detection," Journal of Machine Learning Research, 6, 211-232 (2005).
12. A. Asuncion, and D. J. Newman, [UCI Machine Learning Repository ], Irvine, CA(2007).
13. J. Theiler, "Quantitative comparison of quadratic covariance-based anomalous change detectors," Applied Optics, 47, F12-F26 (2008).
14. Kitware Inc., [Visualization ToolKit], (2010).