Combined local color and texture analysis of stained cells
Computer Vision, Graphics, and Image Processing
Spatial frequency channels and perceptual grouping in texture segregation
Computer Vision, Graphics, and Image Processing - Special issue on human and machine vission, part II
A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automated Inspection of Textile Fabrics Using Textural Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Pattern Recognition and Computer Vision
Handbook of Pattern Recognition and Computer Vision
Universal Analytical Forms for Modeling Image Probabilities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture image segmentation using combined features from spatial and spectral distribution
Pattern Recognition Letters
Multi-class feature selection for texture classification
Pattern Recognition Letters
Rotation-invariant and scale-invariant Gabor features for texture image retrieval
Image and Vision Computing
Supervised Texture Classification Using Characteristic Generalized Gaussian Density
Journal of Mathematical Imaging and Vision
Texture Measures for Automatic Classification of Pulmonary Disease
IEEE Transactions on Computers
Efficient distance-based per-pixel texture classification with Gabor wavelet filters
Pattern Analysis & Applications - Special Issue: Non-parametric distance-based classification techniques and their applications
IEEE Transactions on Image Processing
Wavelet-based statistical signal processing using hidden Markovmodels
IEEE Transactions on Signal Processing
Improved hidden Markov models in the wavelet-domain
IEEE Transactions on Signal Processing
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
Comparison of texture features based on Gabor filters
IEEE Transactions on Image Processing
Texture classification using spectral histograms
IEEE Transactions on Image Processing
Wavelet-based level set evolution for classification of textured images
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Image and Texture Segmentation Using Local Spectral Histograms
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
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
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
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
| Hi-index | 0.01 |
In this correspondence, we propose a novel, efficient, and effective Refined Histogram (RH) for modeling the wavelet subband detail coefficients and present a new image signature based on the RH model for supervised texture classification. Our RH makes use of a step function with exponentially increasing intervals to model the histogram of detail coefficients, and the concatenation of the RH model parameters for all wavelet subbands forms the so-called RH signature. To justify the usefulness of the RH signature, we discuss and investigate some of its statistical properties. These properties would clarify the sufficiency of the signature to characterize the wavelet subband information. In addition, we shall also present an efficient RH signature extraction algorithm based on the coefficient-counting technique, which helps to speed up the overall classification system performance. We apply the RH signature to texture classification using the well-known databases. Experimental results show that our proposed RH signature in conjunction with the use of symmetrized Kullback-Leibler divergence gives a satisfactory classification performance compared with the current state-of-the-art methods.