Imtnan-Ul-Haque Qazi

Parametric models of linear prediction error distribution for color texture and satellite image segmentation

In this article we present a Bayesian color texture segmentation framework based on the multichannel linear prediction error. Two-dimensional causal and non-causal real (in RGB color space) and complex (in IHLS and L^*a^*b^* color spaces) multichannel linear prediction models are used to characterize the spatial structures in color images. The main contribution of this segmentation methodology resides in the robust parametric approximations proposed for the multichannel linear prediction error distribution. These are composed of a unimodal approximation based on the Wishart distribution and a multimodal approximation based on the multivariate Gaussian mixture models. For the spatial regularization of the initial class label estimates, computed through the proposed parametric priors, we compare the conventional Potts model to a Potts model fusioned with a region size energy term. We provide performances of the method when using Iterated Conditional Modes algorithm and simulated annealing. Experimental results for the segmentation of synthetic color textures as well as high resolution QuickBird and IKONOS satellite images validate the application of this approach for highly textured images. Advantages of using these priors instead of classical Gaussian approximation and improved label field model are shown by these results. They also verify that the L^*a^*b^* color space exhibits better performance among the used color spaces, indicating its significance for the characterization of color textures through this approach.

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 “combined chrominance” 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.

Parametric and Non-parametric Models of Linear Prediction Error for Color Texture Segmentation

This paper presents a comparison of parametric and non-parametric models of multichannel linear prediction error for supervised color texture segmentation. Information of both luminance and chrominance spatial variation feature cues are used to characterize color textures. The method presented consists of two steps. In the first step, we estimate the linear prediction errors of color textures computed on small training sub images. Multichannel complex versions of linear prediction models are used as image observation models in RGB, IHLS and L*a*b* color spaces. In the second step, overall color distribution of the image is estimated from the multichannel prediction error sequences. Both parametric and non-parametric approaches are used for this purpose. A multivariate Gaussian probability approximation is used as the parametric law defining this color distribution. For non-parametric approximation, we have used a multivariate version of k-nearest neighbor algorithm. Error rate, based on well classified pixels, for different linear prediction models using different color spaces are compared and discussed.