A Multivariate Gaussian Mixture Model of linear prediction error for colour texture segmentation

This paper presents an algorithm for parametric supervised colour texture segmentation using a novel image observation model. The proposed segmentation algorithm consists of two phases: In the first phase, we estimate an initial class label field of the image based on a 2D multichannel complex linear prediction model. Information of both luminance and chrominance spatial variation feature cues are used to characterize colour textures. Complex multichannel version of 2D Quarter Plane Autoregressive model is used to model these spatial variations of colour texture images in CIE L*a*b* colour space. Overall colour distribution of the image is estimated from the multichannel prediction error sequence of this Autoregressive model. Another significant contribution of this paper is the modelling of this multichannel error sequence using Multivariate Gaussian Mixture Model instead of a single Gaussian probability. Gaussian parameters are calculated through Expectation Maximization on a training dataset. In second phase of the algorithm, initial class label field obtained through the first stage is spatially regularized by ICM algorithm to have the final segmented image. Visual and quantitative results for different number of components of Multivariate Gaussian Mixture Model are presented and discussed.