Distance measures for signal processing and pattern recognition
Signal Processing
Texture Features for Browsing and Retrieval of Image Data
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
Quaternion color texture segmentation
Computer Vision and Image Understanding
Spatial structure characterization of textures in IHLS colour space
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
A 3D-polar coordinate colour representation well adapted to image analysis
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
Scalable data parallel algorithms for texture synthesis using Gibbs random fields
IEEE Transactions on Image Processing
Computer Vision and Image Understanding
Color texture analysis based on fractal descriptors
Pattern Recognition
Multi-model approach for multicomponent texture classification
ICISP'12 Proceedings of the 5th international conference on Image and Signal Processing
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In this article, a linear prediction model based approach for color texture characterization and classification in the improved hue luminance and saturation color space is presented. Pure chrominance structure information is used in addition to the normally used luminance structure information for color texture classification. Hue and saturation channels of a color image in IHLS color space are combined using a complex exponential to give a single channel which holds all the chrominance information of the image. Two dimensional complex multichannel versions of the non-symmetric half plane autoregressive model, the quarter plane autoregressive model and the Gauss Markov random field model are used to perform parametric power spectrum estimation of both luminance and the ''combined chrominance'' channels of the image. The accuracy and precision of these spectral estimates are proven quantitatively by performing tests on a large number of images. Spectral distance measures are calculated for the spectral information of luminance and chrominance channels individually as well as combined through a combination coefficient. Using these distance measures, color texture classification is done with k-nearest neighbor algorithm. Experimental results verify that the IHLS color space exhibits better performance than the RGB color space indicating the significance of using IHLS for such analysis. They also show that color texture characterization and percentage classification obtained by combined luminance and chrominance structure information is better than the color texture classification done using only the luminance structure information.