Color spectral analysis for spatial structure characterization of textures in IHLS color space |
| |
Authors: | Imtnan-Ul-Haque Qazi [Author Vitae] Olivier Alata [Author Vitae] Christine Fernandez-Maloigne [Author Vitae] |
| |
Affiliation: | a Laboratory XLIM Department SIC, UMR CNRS 6172, University of Poitiers, bat. SP2MI, av. Marie et Pierre Curie, 86960 Chasseneuil-Futuroscope Cedex, France b Laboratory L3I, University of La Rochelle, avenue Michel Crepeau, 17042 La Rochelle Cedex 1, France |
| |
Abstract: | 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. |
| |
Keywords: | Chrominance structure information Parametric spectrum estimation 2D multichannel random field modeling Linear prediction models Color texture classification Fusion of luminance and chrominance spectra |
本文献已被 ScienceDirect 等数据库收录! |
|