This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Generalization Capabilities of Subtle Image Pattern Classifiers
April 1992 (vol. 4 no. 2)
pp. 172-177

The generalization capabilities, for learned subtle image pattern categories, of neural network and algorithmic classification techniques are described. Several neural network and algorithmic techniques have been applied to a set of feature vectors extracted from thermal infrared images which characterize the extent of whiplash injury. Thermography recently has been reported to have clinical utility in a multitude of neuromusculoskeletal disorders, particularly with soft tissue injuries such as whiplash in which there are few widely agreed upon diagnostic standards. The results of this research indicate that the backpropagation neural network produces the best classification results and provides significantly better generalization from a set of training patterns. Results of unsupervised classification of the data using clustering algorithms and the Adaptive Resonance Theory (ART2) neural network demonstrate the difficulties of learning and of generalization of patterns from such data.

[1] D. E. Rumelhart and J. L. McClelland,Parallel Distributed Processing. Cambridge, MA: MIT Press, 1985.
[2] G. A. Carpenter and S. Grossberg, "A massively parallel architecture for a self-organizing neural pattern recognition machine,"Computer Vision, Graphics, and Image Processing, vol. 37, pp. 54-115, 1987.
[3] D. D. Egbert, E. Rhodes and P. H. Goodman, "Preprocessing of biomedical images for neurocomputer analysis," inProc. IEEE 1988 Int. Conf. Neural Networks, July 24-27, 1988.
[4] D. D. Egbert, V. G. Kaburlasos, and P. H. Goodman, "Neural network discrimination of subtle image patterns," inProc. IEEE/INNS Int. Joint Conf. Neural Networks, San Diego, CA, June 17-21, 1990, pp. 1517-1524.
[5] V. G. Kaburlasos, "Neurocomputing classification of biomedical image patterns," MS thesis, (advisor, D. D. Egbert), Univ. of Nevada, Reno, NV, Nov. 1989.
[6] S. Uematsu, "Symmetry of skin temperature comparing one side of the body to the other,"Thermology, vol. I, no. 1, 1985.
[7] P. H. Goodman, M. Murphy, G. Siltanen, M. Kelly and L. Rucker, "Normal temperature asymmetry of the back and extremities by computer-assisted infrared imaging,"Thermology, vol. I, no. 4, 1986.
[8] G. A. Carpenter and S. Grossberg, "ART 2: Self-Organization of stable category recognition codes for analog input patterns,"Appl. Opt., vol. 26, no. 23, pp. 4919-4930, Dec. 1987.
[9] P. Gallinari, S. Thiria, F. Badrin, and F. Fogelman-Soule, "On the relations between discriminant analysis and multilayer perceptrons,"Neural Networks, vol. 4, no. 3, pp. 349-360, 1991.
[10] G. A. Carpenter and S. Grossberg, "ART 3: Hierarchical search using chemical transmitters in self-organizing pattern recognition architectures,"Neural Networks, vol. 3, no. 2, pp. 129-152, 1990.
[11] G. A. Carpenter and S. Grossberg, "ARTMAP: Supervised real-time learning and classification of nonstationary data by a self-organizing neural network,"Neural Networks, vol. 4, no. 5, pp. 565-588, 1991.

Index Terms:
ART2 neural network; generalization; image pattern classifiers; image pattern categories; neural network; algorithmic classification techniques; feature vectors; thermal infrared images; whiplash injury; neuromusculoskeletal disorders; soft tissue injuries; backpropagation neural network; training patterns; unsupervised classification; learning systems; neural nets; pattern recognition; picture processing
Citation:
D.D. Egbert, P.H. Goodman, V.G. Kaburlasos, J.H. Witchey, "Generalization Capabilities of Subtle Image Pattern Classifiers," IEEE Transactions on Knowledge and Data Engineering, vol. 4, no. 2, pp. 172-177, April 1992, doi:10.1109/69.134255
Usage of this product signifies your acceptance of the Terms of Use.