Distance measures for signal processing and pattern recognition
Signal Processing
Multiple Resolution Segmentation of Textured Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cluster Analysis by Binary Morphology
IEEE Transactions on Pattern Analysis and Machine Intelligence
Chromatic correlation features for texture recognition
Pattern Recognition Letters
Experiments in colour texture analysis
Pattern Recognition Letters
Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions
SIAM Journal on Optimization
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Outex - New Framework for Empirical Evaluation of Texture Analysis Algorithms
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Comparison of Colour Spaces for Optic Disc Localisation in Retinal Images
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Computer Vision and Image Understanding
A Markov random field model for mode detection in cluster analysis
Pattern Recognition Letters
Color Constancy
Computer Vision and Image Understanding
A 3D-polar coordinate colour representation well adapted to image analysis
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
An analytic distance metric for Gaussian mixture models with application in image retrieval
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Perceptually uniform color spaces for color texture analysis: an empirical evaluation
IEEE Transactions on Image Processing
Computer Vision and Image Understanding
Color texture analysis based on fractal descriptors
Pattern Recognition
Performance analysis of colour descriptors for parquet sorting
Expert Systems with Applications: An International Journal
An ellipsoidal model for generating realistic 3D facial textures
International Journal of Computer Applications in Technology
Color texture analysis using CFA chromatic co-occurrence matrices
Computer Vision and Image Understanding
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This article presents a comparison of different color spaces including RGB, IHLS and L@?a*b* for color texture characterization. This comparison is based on the fusion of the independent spatial structure and color feature cues. In IHLS and L*a*b*, two channel complex color images are created from the luminance and the chrominance values. For such images, two dimensional complex multichannel linear prediction models are used to perform parametric power spectrum estimation and the structure feature cues are computed from this estimated power spectrum. Quantitative comparison of auto spectra of luminance and combined chrominance channels for different color spaces is done. This comparison is based on the degree of decorrelation between luminance and chrominance information provided by different color space transformations. Three dimensional histograms are used as color feature cues. Then, to classify color textures, Kullback-Leibler divergence based symmetric distance measures are calculated for pure color, luminance structure and chrominance structure feature cues. Individual as well as combined effect of information from all feature cues on classification results is then compared for different color spaces and different color texture data sets. The proposed color texture classification method performs better than the state of the art methods in certain cases. The L*a*b* color space gives us a better characterization of the chrominance spatial structure as well as the overall spatial structure for all of the chosen data sets. Experimental results on pixel classification of color textures are also presented and discussed.