Texture Classification by Wavelet Packet Signatures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture classification using ridgelet transform
Pattern Recognition Letters
Supervised Texture Classification Using Characteristic Generalized Gaussian Density
Journal of Mathematical Imaging and Vision
IEEE Transactions on Image Processing
Texture classification by modeling joint distributions of local patterns with Gaussian mixtures
IEEE Transactions on Image Processing
Texture classification using refined histogram
IEEE Transactions on Image Processing
Texture classification based on contourlet subband clustering
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
Statistical texture characterization from discrete wavelet representations
IEEE Transactions on Image Processing
Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance
IEEE Transactions on Image Processing
Texture classification using spectral histograms
IEEE Transactions on Image Processing
SVD-Based Modeling for Image Texture Classification Using Wavelet Transformation
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Texture classification and segmentation using wavelet frames
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
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In information processing using Wavelet transform, wavelet subband coefficients are often modelled by a probability distribution function. Recently, a local energy histogram method has been proposed to alleviate the difficulty in modeling wavelet subband coefficients with a previously assumed distribution function. Actually, the similarity between any two local energy histograms was measured by a symmetrized Kullback-Leibler divergence (SKLD). However, this measurement neglects the balance of wavelet subbands' roles in texture classification. In this paper, we propose an efficient texture classification method based on weighted symmetrized Kullback-Leibler divergences (WSKLDs) between two local energy histograms (LEHs). In particular, for any test and training images, we index their Wavelet subbands in the same way, and weight the SKLD between any two LEHs of the s-th wavelet subbands of two image by the reciprocal of the summation of the SKLDs between the expected LEHs of any two different texture classes over all training images. Experimental results reveal that our proposed method outperforms five state-of-the-art methods.