Empirical capacity of a recognition channel for single- and multipose object recognition under the constraint of PCA encoding

IEEE Transactions on Image Processing

Volumn 18, Issue 3, 2009, Pages 636-651

  Chen, Xiaohan ^a   Schmid, Natalia A ^a  

^a West Virginia University   (United States)

Author keywords

Empirical capacity; Empirical random coding bound; Object recognition; Principal component analysis (PCA); Recognition channel

Indexed keywords

CHANNEL CAPACITY; FEATURE EXTRACTION; OBJECT RECOGNITION; PROBABILITY DENSITY FUNCTION; STATISTICAL TESTS;

ELEVATION ANGLES; EMPIRICAL CAPACITY; EMPIRICAL RANDOM CODING BOUND; ESTIMATED VARIANCES; FEATURE EXTRACTION TECHNIQUES; GAUSSIAN DISTRIBUTED; GOODNESS-OF-FIT TESTS; MULTI POSE; MULTIPLE IMAGES; OBJECT CLASS; OBJECT DATABASE; OBJECT RECOGNITION SYSTEMS; ORIGINAL IMAGES; PRINCIPAL COMPONENT ANALYSIS (PCA); PROBABILITY OF ERRORS; RANDOM CODING EXPONENTS; RECOGNITION CAPACITIES; RECOGNITION CHANNEL; RECOGNITION RATES; RECOGNITION SYSTEMS;

PRINCIPAL COMPONENT ANALYSIS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; INFORMATION RETRIEVAL; METHODOLOGY; PRINCIPAL COMPONENT ANALYSIS; STATISTICAL ANALYSIS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DATA INTERPRETATION, STATISTICAL; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; INFORMATION STORAGE AND RETRIEVAL; PATTERN RECOGNITION, AUTOMATED; PRINCIPAL COMPONENT ANALYSIS; SUBTRACTION TECHNIQUE;

EID: 61349179043     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2008.2010635     Document Type: Article

References (29)

1. N. A. Schmid and J. A. O'Sullivan, "Performance prediction methodology for biometric systems using a large deviations approach," IEEE Trans. Signal Process., vol. 52, no. 10, pp. 3036-3045, Oct 2004.
2. M. B. Westover and J. A. O'Sullivan, "Towards an information theoretic framework for object recognition," in Proc. Int. Symp. Information Theory (ISIT), Chicago, IL, 2004, p. 219.
3. M. B. Westover and J. A. O'Sullivan, "Achievable rates for pattern recognition: Binary and Gaussian cases," in Proc. Int. Symp. Information Theory (ISIT), Adelaide, Australia, 2005, pp. 28-32.
4. R. G. Gallager, Information Theory and Reliable Communications. New York: Wiley, 1968.
5. T. M. Cover and J. A. Thomas, Elements of Information Theory. New York: Wiley, 1991.
6. R. E. Blahut, Principles and Practice of Information Theory. New York: Addison-Wesley, 1987.
7. X. Chen and N. A. Schmid, "On capacity of automatic target recognition systems," in Proc. 41st Annu. Conf. Information Sciences and Systems Mar. 14-16, 2007, pp. 530-534.
8. X. Chen and N. A. Schmid, "On empirical capacity, random coding bound, and probability of outage of an object recognition system under constraint of pca-encoding," in Proc. 42nd Annu. Conf. Information Sciences and Systems, Mar. 19-21, 2008, pp. 967-971.
9. [Online]. Available: http://www1.cs.columbia.edu/CAVE/software/softlib/ coil 100.php
10. [Online]. Available: http://www.frvt.org/FRGC/
11. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification. New York: Wiley, 2001.
12. L. Devroye, L. Gyorfi, and G. Lugosi, A Probabilistic Theory of Pattern Recognition. New York: Springer, 1996.
13. A. K. Jain, R. P. W. Duin, and J. Mao, "Statistical pattern recognition: A review," IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 1, pp. 4-37, Jan. 2000.
14. K. Fukunaga, Introduction to Statistical Pattern Recognition. New York: Academic, 1990.
15. H. L. V. Trees, Detection, Estimation, and Modulation Theory, Part I New York: Wiley, 2001.
16. R. M. Gray, Entropy and Information Theory. New York: Springer-Verlag, 1990.
17. C. E. Shannon, "Probability of error for optimal codes in a Gaussian channel," Bell Syst. Tech. J., vol. 38, no. 3, pp. 611-656, 1959.
18. I. Jolliffe, Principal Component Analysis. New York: Springer-Verlag, 1986.
19. M. Kendall, Multivariate Analysis. London, U.K.: Charles Griffin, 1975.
20. J. A. O'Sullivan and N. A. Schmid, "Performance analysis of physical signature authentication," IEEE Trans. Inf. Theory, vol. 47, no. 7, pp. 3034-3039, Nov 2001.
21. X. Chen and N. A. Schmid, "On the limits of target recognition in the presence of atmospheric effects," in Proc. SPIE Symp. Defense and Security, 2008, vol. 6968, p. 6968-17.
22. S. W. Smith, The Scientist and Engineer's Guide to Digital Signal Processing. Pasadena, CA: California Technical Publishing, 1997.
23. A. K. Jain, Fundamentals of Digital Image Processing. Englewood Cliffs, NJ: Prentice-Hall, 1989.
24. A. E. Savakis and H. J. Trussell, "Blur identification by residual spectral matching," IEEE Trans. Image Process., vol. 2, pp. 141-151, Apr. 1993.
25. W. J. Conover, Practical Nonparametric Statistics, 3rd ed. New York: Wiley, 1999.
26. S. Shapiro and M. Wilk, "An analysis of variance test for normality," Biometrika, vol. 52, no. 3-4, pp. 591-611, Dec. 1965.
27. O. J. Dunn and V. A. Clark, Applied Statistics: Analysis of Variance and Regression. New York: Wiley, 1974.
28. J. A. O'Sullivan, R. E. Blahut, and D. L. Snyder, "Information-theoretic image formation," IEEE Trans. Inf. Theory vol. 44, no. 6, pp. 2094-2013, Oct 1998.
29. J. H. Friedman, "On bias, variance, 0/1-loss, and the curse-of-dimensionality," Data Mining Knowl. Disc, vol. 1, no. 1, pp. 55-77, 1997.