Texture descriptors based on co-occurrence matrices
Computer Vision, Graphics, and Image Processing
Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Classification by Wavelet Packet Signatures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy clustering with volume prototypes and adaptive cluster merging
IEEE Transactions on Fuzzy Systems
A recursive thresholding technique for image segmentation
IEEE Transactions on Image Processing
Extended fractal analysis for texture classification and segmentation
IEEE Transactions on Image Processing
Wavelet-based rotational invariant roughness features for texture classification and segmentation
IEEE Transactions on Image Processing
Comparison of texture features based on Gabor filters
IEEE Transactions on Image Processing
Texture classification using spectral histograms
IEEE Transactions on Image Processing
Texture analysis and classification with tree-structured wavelet transform
IEEE Transactions on Image Processing
Texture classification and segmentation using wavelet frames
IEEE Transactions on Image Processing
A novel semi-supervised fuzzy C-means clustering method
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Texture classification using refined histogram
IEEE Transactions on Image Processing
Unsupervised texture-based image segmentation through pattern discovery
Computer Vision and Image Understanding
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Texture discrimination is playing a vital role in a real world image classification and object identification in a content based image retrieval (CBIR) system. For discriminating the textures, exact features have to be extracted. Although there are many techniques available they are not capable of classifying the universal textures because of their inherent limitations. In this paper, a novel method is introduced to extract the features by combining the texture discriminating features of spatial and spectral distribution of image attributes, and a comparison is made with the popular Gaussian and Gabor wavelets based methods for segmenting the image. The segmented outputs and the classification efficiency of the proposed method are found to be better and the time taken is reasonable.