Add to ORKGThis paper addresses the issue of selecting features from a given wavelet packet subband decomposition that are most useful for texture classification in an image. A functional measure based on Kullback-Leibler distance is proposed as a way to select most discriminant subbands. Experimental results show a superior performance in terms of classification error rates
Classification results obtained using wavelet-based texture analysis techniques vary with the choice...
In this letter, we propose an efficient one-nearest-neighbor classifier of texture via the contrast ...
The contourlet transform was recently developed to overcome the limitations of the wavelet transform...
This paper addresses the issue of selecting features from a given wavelet packet subband decompositi...
Wavelet packets are well-known for their ability to compactly represent textures consiting of oscill...
The subband histograms of wavelet packet bases adapted to individual texture classes often fail to d...
This correspondence introduces a new approach to characterize textures at multiple scales. The perfo...
Although subband histograms of the wavelet coefficients of natural images possess a characteristic l...
Abstract- This correspondence introduces a new approach to char-acterize textures at multiple scales...
Feature extraction is an important processing procedure in texture classification. For feature extra...
In information processing using Wavelet transform, wavelet subband coefficients are often modelled b...
Feature extraction is an important processing procedure in texture classification. For feature extra...
Performance of the proposed methods is demonstrated in extensive experiments, which justify the new ...
Wavelets based analysis has been used frequently in literature for texture analysis and features ext...
This paper presents a novel multiresolution approach to the classification of textures. The approach...
Classification results obtained using wavelet-based texture analysis techniques vary with the choice...
In this letter, we propose an efficient one-nearest-neighbor classifier of texture via the contrast ...
The contourlet transform was recently developed to overcome the limitations of the wavelet transform...
This paper addresses the issue of selecting features from a given wavelet packet subband decompositi...
Wavelet packets are well-known for their ability to compactly represent textures consiting of oscill...
The subband histograms of wavelet packet bases adapted to individual texture classes often fail to d...
This correspondence introduces a new approach to characterize textures at multiple scales. The perfo...
Although subband histograms of the wavelet coefficients of natural images possess a characteristic l...
Abstract- This correspondence introduces a new approach to char-acterize textures at multiple scales...
Feature extraction is an important processing procedure in texture classification. For feature extra...
In information processing using Wavelet transform, wavelet subband coefficients are often modelled b...
Feature extraction is an important processing procedure in texture classification. For feature extra...
Performance of the proposed methods is demonstrated in extensive experiments, which justify the new ...
Wavelets based analysis has been used frequently in literature for texture analysis and features ext...
This paper presents a novel multiresolution approach to the classification of textures. The approach...
Classification results obtained using wavelet-based texture analysis techniques vary with the choice...
In this letter, we propose an efficient one-nearest-neighbor classifier of texture via the contrast ...
The contourlet transform was recently developed to overcome the limitations of the wavelet transform...