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Proceedings Paper

Wavelet transform and SGLDM: a classification performance study using ML parameter estimation, minimum distance, and k-nearest-neighbor classifiers
Author(s): Reena Singh; Ramon E. Vasquez; Rajeev Singh
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Paper Abstract

This paper presents a comparative study of the performance of the spatial gray level dependence method (SGLDM) and the wavelet transform (WT) method using the three prevalent classifiers, maximum likelihood estimation, minimum distance classifier and the k-nearest neighbor. The features have been extracted using a tree-structured wavelet transform. Daubechies filters have been used for the composition. For SGLDM, experiments were performed to come up with an optimum combination of distance and angle for computing features. The criteria chosen for comparison is the classification accuracy under the constraints of the same sample size, same number of training and test samples, and same number of features. The results indicate that the maximum-likelihood classifier and the minimum distance function gave comparable results for the wavelet transform method. The k-nearest neighbor classifier gave the highest classification accuracy for the wavelet transform method but performed poorly for the SGLDM. Maximum-likelihood classifier performed better for the wavelet transform algorithm than the SGLDM. The minimum distance classifier did not prove to be powerful for the SGLDM.

Paper Details

Date Published: 22 July 1997
PDF: 9 pages
Proc. SPIE 3074, Visual Information Processing VI, (22 July 1997); doi: 10.1117/12.280615
Show Author Affiliations
Reena Singh, Univ. of Puerto Rico/Mayaguez (United States)
Ramon E. Vasquez, Univ. of Puerto Rico/Mayaguez (United States)
Rajeev Singh, Univ. of Puerto Rico/Mayaguez (United States)


Published in SPIE Proceedings Vol. 3074:
Visual Information Processing VI
Stephen K. Park; Richard D. Juday, Editor(s)

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