An Evaluation of Algorithms for Single-Echo Biosonar Target Classification
A recent neurospiking coding scheme for feature extraction from biosonar echoes of various plants is examined with avariety of stochastic classifiers. Feature vectors derived are employedin well-known stochastic classifiers, including nearest-neighborhood,single Gaussian and a Gaussian mixture with EM optimization.Classifiers' performances are evaluated by using cross-validation and bootstrapping techniques. It is shown that the various classifers perform equivalently and that the modified preprocessing configuration yields considerably improved results.
[1] P. McKerrow, and N. Harper, ''Plant acoustic density profile model of CTFM ultrasonic sensing,'' IEEE Sensors Journal, vol. 1, no. 4, pp.245-255, Dec. 2001.[2] R. Kuc, ''Bimimetic sonar locates and recognizes objects,'' IEEE J.Oceanic Engineering, vol. 22, no. 4, pp. 616-624, Oct. 1997.[3] R. Kuc, ''Transforming echoes into pseudo-action potentials for classifying plants,'' J. Acoust. Soc. Am., vol. 110, no. 4, pp. 2198-2206,Oct. 2001.[4] R. M├╝ller, and R. Kuc, ''A parsimonious signal representation of random echoes based on a biomimetic spike code,'' in Proc. ICSC Symp. On Intelligent Systems and Applications, pp. 915-921,Canada,May 2000.[5] R. M├╝ller, ''A computational theory for the classification natural biosonar targets based on a spike code,'' Network: Comput. Neural Syst., vol. 14, pp. 595-612, May 2003.[6] C. Therrien, Decision, Estimation and Classification, An Introduction to Pattern Recognition and Other Related Topics, John wiley & Sons,1989.[7] T. M. Cover, and P. E. Hart, ''Nearest neighbor pattern classification,''IEEE Trans. Information Theory, vol. IT-13, pp. 21-27, Jan. 1967.[8] H. Akaike, ''A new look at the statistical model identification,'' IEEE Trans. Automatic Control, vol. 19, no. 6, pp. 716-723, Dec. 1974.[9] J. Rissanen, ''Stochastic complexity and modeling,'' The Annals of Statistics, vol. 13, no. 3, pp. 1080-1100, 1986.[10] A. P. Demspter, N. A. Laird, and D. B. Rubin, ''Maximum likelihood from incomplete data via the EM algorithm,'' Journal of the Royal Statistical Society Series B, vol. 39, pp. 1-38, 1977.[11] C. M. Bishop, Neural Networks for Pattern Recognition, Oxford Univ.Press, 1995.[12] Y. Linde, A. Buzo, and R. M. Gray, ''An algorithm for vector quantizer design,'' IEEE Trans. Commun., vol. COM-28, pp. 84-95, Dec. 1980.
[1] P. McKerrow, and N. Harper, ''Plant acoustic density profile model of CTFM ultrasonic sensing,'' IEEE Sensors Journal, vol. 1, no. 4, pp.245-255, Dec. 2001.[2] R. Kuc, ''Bimimetic sonar locates and recognizes objects,'' IEEE J.Oceanic Engineering, vol. 22, no. 4, pp. 616-624, Oct. 1997.[3] R. Kuc, ''Transforming echoes into pseudo-action potentials for classifying plants,'' J. Acoust. Soc. Am., vol. 110, no. 4, pp. 2198-2206,Oct. 2001.[4] R. M├╝ller, and R. Kuc, ''A parsimonious signal representation of random echoes based on a biomimetic spike code,'' in Proc. ICSC Symp. On Intelligent Systems and Applications, pp. 915-921,Canada,May 2000.[5] R. M├╝ller, ''A computational theory for the classification natural biosonar targets based on a spike code,'' Network: Comput. Neural Syst., vol. 14, pp. 595-612, May 2003.[6] C. Therrien, Decision, Estimation and Classification, An Introduction to Pattern Recognition and Other Related Topics, John wiley & Sons,1989.[7] T. M. Cover, and P. E. Hart, ''Nearest neighbor pattern classification,''IEEE Trans. Information Theory, vol. IT-13, pp. 21-27, Jan. 1967.[8] H. Akaike, ''A new look at the statistical model identification,'' IEEE Trans. Automatic Control, vol. 19, no. 6, pp. 716-723, Dec. 1974.[9] J. Rissanen, ''Stochastic complexity and modeling,'' The Annals of Statistics, vol. 13, no. 3, pp. 1080-1100, 1986.[10] A. P. Demspter, N. A. Laird, and D. B. Rubin, ''Maximum likelihood from incomplete data via the EM algorithm,'' Journal of the Royal Statistical Society Series B, vol. 39, pp. 1-38, 1977.[11] C. M. Bishop, Neural Networks for Pattern Recognition, Oxford Univ.Press, 1995.[12] Y. Linde, A. Buzo, and R. M. Gray, ''An algorithm for vector quantizer design,'' IEEE Trans. Commun., vol. COM-28, pp. 84-95, Dec. 1980.
@article{"International Journal of Information, Control and Computer Sciences:50118", author = "Turgay Temel and John Hallam", title = "An Evaluation of Algorithms for Single-Echo Biosonar Target Classification", abstract = "A recent neurospiking coding scheme for feature extraction from biosonar echoes of various plants is examined with avariety of stochastic classifiers. Feature vectors derived are employedin well-known stochastic classifiers, including nearest-neighborhood,single Gaussian and a Gaussian mixture with EM optimization.Classifiers' performances are evaluated by using cross-validation and bootstrapping techniques. It is shown that the various classifers perform equivalently and that the modified preprocessing configuration yields considerably improved results.
", keywords = "Classification, neuro-spike coding, non-parametricmodel, parametric model, Gaussian mixture, EM algorithm.", volume = "1", number = "1", pages = "1-4", }
