Intrinsic generalization analysis of low dimensional representations
Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
Visually Distinct Patterns with Matching Subband Statistics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reduced Complexity Rotation Invariant Texture Classification Using a Blind Deconvolution Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture image segmentation using combined features from spatial and spectral distribution
Pattern Recognition Letters
Visual similarity based document layout analysis
Journal of Computer Science and Technology - Special section on China AVS standard
Supervised Texture Classification Using Characteristic Generalized Gaussian Density
Journal of Mathematical Imaging and Vision
Cognitive image representation based on spectrum pyramid decomposition
MMACTEE'08 Proceedings of the 10th WSEAS International Conference on Mathematical Methods and Computational Techniques in Electrical Engineering
Blur Insensitive Texture Classification Using Local Phase Quantization
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
Optimizing Gabor Filter Design for Texture Edge Detection and Classification
International Journal of Computer Vision
ISICA'07 Proceedings of the 2nd international conference on Advances in computation and intelligence
Texture classification by modeling joint distributions of local patterns with Gaussian mixtures
IEEE Transactions on Image Processing
A new approach for texture classification in CBIR
International Journal of Computer Applications in Technology
Texture classification using refined histogram
IEEE Transactions on Image Processing
Variational region-based segmentation using multiple texture statistics
IEEE Transactions on Image Processing
Contourlet-based texture classification with product bernoulli distributions
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Applying preattentive visual guidance in document image analysis
IWICPAS'06 Proceedings of the 2006 Advances in Machine Vision, Image Processing, and Pattern Analysis international conference on Intelligent Computing in Pattern Analysis/Synthesis
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
Segmentation and classification of side-scan sonar data
ICIRA'12 Proceedings of the 5th international conference on Intelligent Robotics and Applications - Volume Part I
Multiscale Texture Extraction with Hierarchical (BV,Gp,L2) Decomposition
Journal of Mathematical Imaging and Vision
Multiscale texture segmentation via a contourlet contextual hidden Markov model
Digital Signal Processing
Pattern Recognition Letters
Automatic classification of sleep stages based on the time-frequency image of EEG signals
Computer Methods and Programs in Biomedicine
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Based on a local spatial/frequency representation,we employ a spectral histogram as a feature statistic for texture classification. The spectral histogram consists of marginal distributions of responses of a bank of filters and encodes implicitly the local structure of images through the filtering stage and the global appearance through the histogram stage. The distance between two spectral histograms is measured using χ2-statistic. The spectral histogram with the associated distance measure exhibits several properties that are necessary for texture classification. A filter selection algorithm is proposed to maximize classification performance of a given dataset. Our classification experiments using natural texture images reveal that the spectral histogram representation provides a robust feature statistic for textures and generalizes well. Comparisons show that our method produces a marked improvement in classification performance. Finally we point out the relationships between existing texture features and the spectral histogram, suggesting that the latter may provide a unified texture feature.