This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Infrared-Image Classification Using Hidden Markov Trees
October 2002 (vol. 24 no. 10)
pp. 1394-1398

Abstract—An image of a three-dimensional target is generally characterized by the visible target subcomponents, with these dictated by the target-sensor orientation (target pose). An image often changes quickly with variable pose. We define a class as a set of contiguous target-sensor orientations over which the associated target image is relatively stationary with aspect. Each target is in general characterized by multiple classes. A distinct set of Wiener filters are employed for each class of images, to identify the presence of target subcomponents. A Karhunen-Loeve representation is used to minimize the number of filters (templates) associated with a given subcomponent. The statistical relationships between the different target subcomponents are modeled via a hidden Markov tree (HMT). The HMT classifier is discussed and example results are presented for forward-looking-infrared (FLIR) imagery of several vehicles.

[1] M.S. Crouse, R.D. Novak, and R.G. Baraniuk, “Wavelet-Based Statistical Signal Processing Using Hidden Markov Models,” IEEE Trans. Signal Processing, vol. 46, pp. 886-902, Apr. 1998.
[2] N. Dasgupta, P. Runkle, L. Couchman, and L. Carin, “Dual Hidden Markov Model for Characterizing the Wavelet Coefficients from Multi-Aspect Scattering Data,” Signal Processing, vol. 81, pp. 1303-1316, June 2001.
[3] R.L. Kashyap and R. Chellappa,“Estimation and choice of neighbors in spatial-interaction models of images,” IEEE Trans. Information Theory, vol. 29, no. 1, pp. 60-72, Jan. 1983.
[4] S. Geman and C. Graffigne, “Markov Random Field Image Models and Their Applications to Computer Vision,” Proc. Int'l Congress of Math., pp. 1496-1517, 1986.
[5] D. Nandy and J. Ben-Arie, “Generalized Feature Extraction Using Expansion Matching,” IEEE Trans. Image Processing, vol. 8, no. 1, pp. 22-32, Jan. 1999.
[6] A.K. Jain, Fundamentals of Digital Image Processing. Prentice Hall, 1989.
[7] J. Li, A. Najmi, and R.M. Gray, Image Classification by a Two Dimensional Hidden Markov Model IEEE Trans. Signal Processing, vol. 48, no. 2, pp. 517-33, Feb. 2000.
[8] C. Nilubol, Q.H. Pham, R.M. Mersereau, M.J.T. Smith, and M.A. Clements, “Hidden Markov Modeling for SAR Automatic Target Recognition,” Proc. IEEE Int'l Conf. Acoustics, Speech, and Signal Processing '98, vol. 1, pp. 1061-1064, May 1998.
[9] L.R. Rabiner, “Tutorial on Hidden Markov Model and Selected Applications in Speech Recognition,” Proc. IEEE, vol. 77, no. 2, pp. 257-285, 1989.
[10] A.M.P. Marinelli, L.M. Kaplan, and N.M. Nasrabadi, “SAR ATR Using a Modified Learning Vector Quantization Algorithm,” Proc. SPIE Conf. Algorithms for SAR Imagery VI, pp. 343-354, Apr. 1999.
[11] T. Kohonen, “The Self-Organizing Map,” Proc. IEEE, vol. 78, no. 9, pp. 1464-1480, Sept. 1990.

Index Terms:
Hidden Markov model, infrared imagery, classification.
Citation:
Priya Bharadwaj, Lawrence Carin, "Infrared-Image Classification Using Hidden Markov Trees," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 10, pp. 1394-1398, Oct. 2002, doi:10.1109/TPAMI.2002.1039210
Usage of this product signifies your acceptance of the Terms of Use.