Spatial structure characterization of textures in IHLS colour space

We present model based approaches for colour texture characterization in IHLS colour space. Pure chrominance structure information is used in parallel with luminance structure information for colour texture classification. Hue and saturation channels are combined through a complex exponential to give a single channel which holds all the chrominance information of the image. Two dimensional complex multichannel versions of Non-Symmetric Half Plane Autoregressive model and Gauss Markov Random Field model are used to perform parametric power spectrum estimation of both luminance and the ldquocombined chrominancerdquo channels of the image. Colour texture classification is done using k-nearest neighbor algorithm on spectral distance measures both for luminance and chrominance channels individually as well as combined through a combination coefficient. Experimental results show that colour texture characterization obtained by combined luminance and chrominance structure informations is better than the one obtained by using only luminance structure information.