A parametric copula-based framework for hypothesis testing using heterogeneous data

IEEE Transactions on Signal Processing

Volumn 59, Issue 5, 2011, Pages 2308-2319

  Iyengar, Satish G ^a   Varshney, Pramod K ^a   Damarla, Thyagaraju ^b  

^a Syracuse University   (United States)

^b U S ARMY RESEARCH LABORATORY   (United States)

Author keywords

Copula theory; hypothesis testing; Kullback Leibler divergence; multibiometrics; multimodal signals; multisensor fusion; statistical dependence

Indexed keywords

COPULA THEORY; HYPOTHESIS TESTING; KULLBACK LEIBLER DIVERGENCE; MULTI-MODAL; MULTI-SENSOR FUSION; MULTIBIOMETRICS; STATISTICAL DEPENDENCE;

COMPUTER SIMULATION; FACE RECOGNITION; STATISTICS;

DATA HANDLING;

EID: 79954470196     PISSN: 1053587X     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSP.2011.2105483     Document Type: Article

References (40)

1. R. Cutler and L. Davis, "Look who's talking: Speaker detection using video and audio correlation," in Proc. ICME, 2000, pp. 1589-1592. (Pubitemid 33059248)
2. M. J. Beal, N. Jojic, and H. Attias, "A graphical model for audiovisual object tracking," IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 7, pp. 828-836, 2003.
3. Y. Rui and Y. Chen, "Better proposal distributions: Object tracking using unscented particle filter," in Proc. CVPR, 2001.
4. E. Kidron, Y. Y. Schechner, and M. Elad, "Pixels that sound," in Proc. CVPR, 2005.
5. M. E. Sargin, E. Erzin, Y. Yemez, and A. M. Tekalp, "Lip feature extraction based on audio-visual correlation," in Proc. EUSIPCO, 2005.
6. T. Butz and J. Thiran, "From error probability to information theoretic (multi-modal) signal processing," Signal Process., vol. 85, pp. 875-902, 2005. (Pubitemid 40357042)
7. P. Besson, V. Popovici, J. M. Vesin, J. P. Thiran, and M. Kunt, "Extraction of audio features specific to speech production for multimodal speaker detection," IEEE Trans. Multimedia, vol. 10, pp. 63-73, 2008.
8. J. Fisher, III and T. Darrell, "Speaker association with signal-level audiovisual fusion," IEEE Trans. Multimedia, vol. 6, pp. 406-413, 2004.
9. A. Prati, R. Vezzani, L. Benini, E. Farella, and P. Zappi, "An integrated multi-modal sensor network for video surveillance," in Proc. VSSN, 2005, pp. 95-102.
10. W. Ketchantang, S. Derrode, and L. Martin, "Pearson-based mixture model for color object tracking," Mach. Vis. Appl., vol. 19, pp. 457-466, 2008.
11. Y. Stitou, N. Lasmar, and Y. Berthoumieu, "Copula based multivariate gamma modeling for texture classification," in Proc. ICASSP, 2009.
12. N. Brunel, W. Pieczynski, and S. Derrode, "Copulas in vectorial hidden markov chains for multicomponent image segmentation," in Proc. ICASSP, 2005, pp. 717-720.
13. S. G. Iyengar, P. K. Varshney, and T. Damarla, "On the detection of footsteps using acoustic and seismic sensing," in Proc. Asilomar Conf. Signals, Syst. Comput., 2007, pp. 2248-2252.
14. G. Mercier, G. Moser, and S. B. Serpico, "Conditional copulas for change detection in heterogeneous remote sensing images," IEEE Trans. Geosci. Remote Sens., vol. 46, no. 5, pp. 1428-1441, May 2008.
15. C. Genest and J.-C. Boies, "Detecting dependence with Kendall plots," Amer. Statist., vol. 57, no. 4, pp. 275-284, 2003. (Pubitemid 37424481)
16. D. Mari and S. Kotz, Correlation and Dependence. London, U.K.: Imperial College Press, 2001.
17. J. E. Hurtado, Structural Reliability: Statistical Learning Perspectives. New York: Springer, 2004.
18. H. Joe, Multivariate Dependence and Related Concepts. London, U.K.: Chapman and Hall, 1997.
19. R. B. Nelsen, An Introduction to Copulas. New York: Springer-Verlag, 1999.
20. D. Kurowicka and R. Cooke, Uncertainty Analysis With High Dimensional Dependence Modeling. New York: Wiley, 2006.
21. H. Joe and J. J. Xu, The Estimation Method of Inference Functions for Margins for Multivariate Models Dep. Statist., Univ. British Columbia, 1996.
22. E. Bouyé, V. Durrleman, A. Nikeghbali, G. Riboulet, and T. Roncalli, Copulas for Finance-A Reading Guide and Some Applications SSRN eLibrary, 2000 [Online]. Available: http: //ssrn.com/paper=1032533; http: //ssrn.com/abstract=1032533
23. C. Genest and L. P. Rivest, "Statistical inference procedures for bivariate archimedian copulas," J. Amer. Statist. Assoc., vol. 88, no. 423, pp. 1034-1043, Sep. 1993.
24. C. Genest, B. Rémillard, and D. Beaudoin, "Goodness-of-fit tests for copulas: A review and a power study," Insurance: Math. Econom., vol. 44, pp. 199-213, Apr. 2009.
25. B. W. Silverman, Density Estimation for Statistics and Data Analysis. London, U.K.: Chapman and Hall, 1986.
26. S. Eguchi and J. Copas, "InterpretingKullback-Leibler divergence with the Neyman-Pearson lemma," J. Multivar. Anal., vol. 97, no. 9, pp. 2034-2040, Oct. 2006.
27. M. Studený and J. Vejnarová, "Learning in Graphical Models," in Multiinformation Function as a Tool for Measuring Stachastic Dependence. Cambridge, U.K.: MIT Press, 1999, pp. 261-297.
28. D. Bamber, "The area above the ordinal dominance graph and the area below the receiver operating characteristic graph," Journal of Mathematical Psychology, vol. 12, 1975.
29. R. Gupta and A. Hero, "High rate vector quantization for detection," IEEE Trans. Inf. Theory, vol. 49, no. 8, pp. 1951-1969, 2003.
30. A. Bradley, "The use of the area under the ROC curve in the evaluation of machine learning algorithms," Pattern Recogn., vol. 30, no. 7, pp. 1145-1159, 1997. (Pubitemid 127406521)
31. A. J. Simpson and M. J. Fitter, "What is the best index of detectibility?," Psycholog. Bull., vol. 80, pp. 481-488, 1973.
32. C. Cortes and M. Mohri, AUC Optimization vs. Error Rate Minimization, S. Thrun, L. Saul, and B. Scholkopf, Eds. Cambridge, MA: MIT Press, 2004.
33. NIST Biometric Scores Set 2004 [Online]. Available: http: //www.itl. nist.gov/iad/894.03/biometricscores/
34. M. Figueiredo and A. K. Jain, "Unsupervised learning of finite mixture models," IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, pp. 381-396, 2002. (Pubitemid 35289353)
35. S. C. Dass, K. Nandakumar, and A. K. Jain, "A principled approach to score level fusion in multimodal biometric systems," Audio and Videobased Biometr. Person Authent., 2005.
36. J. Kludas, E. Bruno, and S. Marchand-Maillet, "Can feature information interaction help for information fusion in multimedia problems?," Multimedia Tools and Appl., vol. 42, no. 1, pp. 57-71, Mar. 2009.
37. K. Kryszczuk and A. Drygajlo, "Impact of feature correlations on separation between bivariate normal distributions," in Proc. 19th Int. Conf. Pattern Recogn., 2008, pp. 1-4.
38. N. Poh and S. Bengio, "How do correlation and variance of base-experts affect fusion in biometric authentication tasks?," IEEE Trans. Signal Process., vol. 53, no. 11, pp. 4384-4396, Nov. 2005. (Pubitemid 41622568)
39. O. Koval, S. Voloshynovskiy, and T. Pun, "Analysis of multimodal binary detection systems based on dependent/independent modalities," in Proc. IEEE 9th Workshop on Multimedia Signal Process., 2007.
40. H. Chernoff, "Large-sample theory: Parametric case," Ann. Math. Statist., vol. 27, pp. 1-22, 1956.