IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised texture segmentation using Gabor filters
Pattern Recognition
Learning Texture Discrimination Masks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture classification using wavelet transform
Pattern Recognition Letters
Texture segmentation using wavelet transform
Pattern Recognition Letters
Texture classification using ridgelet transform
Pattern Recognition Letters
Texture classification using Gabor wavelets based rotation invariant features
Pattern Recognition Letters
Texture image retrieval using rotated wavelet filters
Pattern Recognition Letters
Multiscale texture classification using dual-tree complex wavelet transform
Pattern Recognition Letters
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
Quaternionic wavelets for texture classification
Pattern Recognition Letters
Computers and Electrical Engineering
Rotation-invariant texture features from the steered Hermite transform
Pattern Recognition Letters
A novel method for image retrieval based on structure elements' descriptor
Journal of Visual Communication and Image Representation
Texture classification of landsat TM imagery using Bayes point machine
Proceedings of the 51st ACM Southeast Conference
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This paper proposes a supervised multiscale Bayesian texture classifier. The classifier exploits the dual-tree complex wavelet transform (DT-CWT) to obtain complex-valued multiscale representations of training texture samples for each texture class. The high-pass subbands of DT-CWT decomposition of a texture image are used to form a multiscale feature vector representing magnitude and phase features. For computational efficiency, the dimensionality of feature vectors is reduced using principal component analysis (PCA). The class conditional probability density function of low-dimensional feature vectors for each texture class is then estimated by using Parzen-window estimate with identical Gaussian kernels and is used to represent the texture class. A query texture image is classified as the corresponding texture class with the highest a posteriori probability according to a Bayesian inferencing. The superior performance and robustness of the proposed classifier is demonstrated for classifying texture images from image databases. The proposed multiscale texture feature vector extracted from both magnitude and phase of DT-CWT subbands of a query image is also shown to be effective for texture retrieval.