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22 July 1997 Wavelet transform and SGLDM: a classification performance study using ML parameter estimation, minimum distance, and k-nearest-neighbor classifiers
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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.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Reena Singh, Ramon E. Vasquez, and Rajeev Singh "Wavelet transform and SGLDM: a classification performance study using ML parameter estimation, minimum distance, and k-nearest-neighbor classifiers", Proc. SPIE 3074, Visual Information Processing VI, (22 July 1997); https://doi.org/10.1117/12.280615
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