Multiresolution Feature Extraction and Selection for Texture Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks
Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Maximum Likelihood Estimation Methods for Multispectral Random Field Image Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution direction filterbanks: theory, design, and applications
IEEE Transactions on Signal Processing - Part I
Singular value decomposition-based reconstruction algorithm for seismic traveltime tomography
IEEE Transactions on Image Processing
Signal analysis using a multiresolution form of the singular value decomposition
IEEE Transactions on Image Processing
Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance
IEEE Transactions on Image Processing
Design-based texture feature fusion using Gabor filters and co-occurrence probabilities
IEEE Transactions on Image Processing
Texture classification and segmentation using wavelet frames
IEEE Transactions on Image Processing
Probabilistic neural-network structure determination for pattern classification
IEEE Transactions on Neural Networks
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A new algorithm based on wavelet packet transform and singular value decomposition is proposed in this paper for classification of textures in the presence of noise. Lower singular values are affected more by noise than higher singular values, and hence only higher singular values are used to classify textures in the presence of noise. The probability density function of the selected singular values is then modeled as an exponential distribution, and the model parameter for the distribution is estimated using the maximum likelihood estimation technique. The model parameter, one for each subband is used as features for the classification. The classification is carried out using Weighted Probabilistic Neural Networks (WPNN). Compared to conventional probabilistic neural networks, WPNN includes weighting factors between pattern layer and summation layer of the conventional PNN. Performance of the algorithm is compared with `wavelet domain generalized Gaussian density based model' in terms of signal to noise ratio and classification rate. Experimental results prove that the proposed algorithm achieves better classification rate under noisy environment.