Unsupervised texture segmentation using Gabor filters
Pattern Recognition
Texture Features for Browsing and Retrieval of Image Data
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 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
Letters: Complex wavelet based texture classification
Neurocomputing
Bayesian texture classification and retrieval based on multiscale feature vector
Pattern Recognition Letters
Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance
IEEE Transactions on Image Processing
Modified color motif co-occurrence matrix for image indexing and retrieval
Computers and Electrical Engineering
Face recognition using Gabor-based direct linear discriminant analysis and support vector machine
Computers and Electrical Engineering
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This paper proposes a multiscale texture classifier which uses features extracted from both magnitude and phase responses of subbands at different resolutions of the dual-tree complex wavelet transform decomposition of a texture image. The mean and entropy in the transform domain are used to form a feature vector. The proposed method can achieve a high texture classification rate even for small number of samples used in training stage. This makes it suitable for applications where the number of texture samples used in training is very limited. The superior performance and robustness of the proposed classifier is shown for classifying and retrieving texture images from image databases.