Adaptive branch and bound algorithm (ABB) for use on hyperspectral data

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 6233 II, Issue , 2006, Pages

  Nakariyakul, Songyot ^a   Casasent, David P ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

Branch and bound algorithm; Dimensionality reduction; Feature selection; Hyperspectral data; Optimal subset search

Indexed keywords

BRANCH AND BOUND ALGORITHM; DIMENSIONALITY REDUCTION; FEATURE SELECTION; HYPERSPECTRAL DATA; OPTIMAL SUBSET SEARCH;

ADAPTIVE SYSTEMS; COMPUTER SIMULATION; DATABASE SYSTEMS; FEATURE EXTRACTION; FUNCTION EVALUATION; TREES (MATHEMATICS);

ALGORITHMS;

EID: 33748641969     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.666175     Document Type: Conference Paper

References (27)

1. B. Thai, and G. Healey, "Invariant subpixel target identification in hyperspectral imagery," Proc. SPIE, vol. 3717, pp. 14-24, 1999.
2. X.-W. Chen, and D. Casasent, "Feature selection from high-dimensional hyperspectral and polarimetric data for target detection," Proc. SPIE, vol. 5437, pp. 171-178, 2004.
3. D. Casasent, and X.-W. Chen, "Feature reduction and morphological processing for hyperspectral image data," Applied Optics, vol. 43 (2), pp. 227-236, 2004.
4. J. Goutsias, and A. Banerji, "A morphological approach to automatic mine detection problems," IEEE Trans. Aerospace and Electronic Systems, vol. 34(4), pp.1085-1096, 1998
5. M.J. Muasher, and D.A. Landgrebe, "The K-L expansion as an effective feature ordering techniques for limited training sample size," IEEE Trans. Geosci. Remote Sensing, vol. GE-21, pp. 438-441, Oct 1983.
6. K. Chao, P.M. Mehl, M. Kim, and Y.-R. Chen, "Detection of chicken skin tumors by multispectral imaging," Proc. SPIE, vol. 4206, pp. 214-223, 2001.
7. T.C. Pearson, D.T. Wicklow, E.B. Maghirang, F. Xie, and F.E. Dowell, "Detecting aflatoxin in single corn kernels by transmittance and reflectance spectroscopy," Trans. of the ASAE, vol. 44(5), pp.1247-1254, 2001.
8. B. Park, K. C. Lawrence, W. R. Windham, and R. J. Buhr, "Hyperspectral imaging for detecting fecal and ingesta contaminants on poultry carcasses," Trans. of the ASAE, vol. 45(6), pp. 2017-2026, 2002.
9. W. R. Windham, K. C. Lawrence, B. Park, R. J. Buhr, "Visible/NIR spectroscopy for characterizing fecal contamination of chicken carcasses," Trans. of the ASAE, vol. 46(3), pp. 747-751, 2003.
10. D. Casasent, and X.-W. Chen, "Waveband selection for hyperspectral data: optimal feature selection," Proc. SPIE, vol. 5106, pp. 259-270, 2003.
11. S. Nakariyakul, and D. Casasent, "Hyperspectral feature selection and fusion for detection of chicken skin tumors," Proc. SPIE, vol. 5271, pp. 128-139, March 2004.
12. S.G. Kong, Y.-R. Chen, I. Kim, and M. Kim, "Analysis of hyperspectral fluorescence images for poultry skin tumor inspection," Applied Optics, vol. 43(4), pp. 824-833, 2004.
13. B. Park, W.R. Windham, K.C. Lawrence, and D.P. Smith, "Classification of hyperspectral imagery for identifying fecal and ingesta contaminants," Proc. SPIE, vol. 5271, pp. 118-127, 2004.
14. S. Nakariyakul, and D. Casasent, "Hyperspectral ratio feature selection: agricultural product inspection example," Proc. SPIE, vol. 5587, pp. 133-143, 2004.
15. G. W. Heitschmidt, K. C. Lawrence, W. R. Windham, B. Park, and D. P. Smith, "Improved imaging system for fecal detection," Proc. SPIE, vol. 5587, pp. 101-111, 2004.
16. F.J. Ferri, P. Pudil, M. Hatef, and J. Kittler, "Comparative study of techniques for large-scale feature selection," Pattern Recognition in Practice IV, E. Gelsema and L. Kanal, eds., pp. 403-413, 1994.
17. A. Jain, and D. Zongker, "Feature selection: evaluation, application, and small sample performance," IEEE Trans. Pattern Anal. Machine Intell., vol. 19(2), pp. 153-158, 1997.
18. M. Kudo, and J. Sklansky, "Comparision of algorithms that select features for pattern classifiers," Pattern Recognition, vol. 33, pp. 25-41, 2000.
19. A.W. Whitney, "A direct method of nonparametric measurement selection," IEEE Trans. Comput., vol. 20, pp. 1100-1103, 1971.
20. T. Marill, and D.M. Green, "On the effectiveness of receptors in recognition system," IEEE Trans. Inform. Theory, vol. 9, pp. 917-922, 1963.
21. P. Pudil, J. Novovicova, and J. Kittler, "Floating search methods in feature selection," Pattern Recognition Lett., vol. 15, pp. 1119-1125, 1994.
22. K. Fukunaga, Introduction to Statistical Pattern Recognition, second ed. Academic Press Inc., New York, 1992.
23. B. Yu, and B. Yuan, "A more efficient branch and bound algorithm for feature selection," Pattern Recognition, vol. 26, pp. 883-889, 1993.
24. P. Somol, P. Pudil, and J. Kittler, "Fast branch & bound algorithms for optimal feature selection," IEEE Trans. Pattern Anal. Machine Intell., vol. 19, pp. 900-912, 2004.
25. D.J. Newman, S. Hettich, C.L. Blake, and C.J. Merz, UCI Repository of machine learning databases [http://www.ics.uci.edu/-mlearn/MLRepository.htm]]. Dept. of Information and Computer Science, Univ. of California, Irvine, CA, 1998.
26. P. Narendra, and K. Fukunaga, "A branch and bound algorithm for feature subset selection," IEEE Trans. Comput., vol. C-26, pp. 917-922, 1977.
27. X.-W. Chen, "An improved branch and bound algorithm for feature selection," Pattern Recognition Lett., vol. 24, pp. 1925-1933, 2003.