Nonlinear prediction for Gaussian mixture image models Prediction is an essential operation in many image processing applications, such as object detection and image and video compression. When the image is modeled as Gaussian, the optimal predictor is linear and easy to obtain. However, image texture and clutter are often nonGaussian and in such cases, optimal predictors are difficult to find. In this paper, we have derived an optimal predictor for an important class of nonGaussian image models, the blockbased multivariate Gaussian mixture model. This predictor has a special nonlinear structure: it is a linear combination of the neighboring pixels but the combination coefficients are also functions of the neighboring pixels, not constants. The efficacy of this predictor is demonstrated in object recognition experiments where the prediction error image is used to identify hidden objects. Results indicate that when the background texture is nonlinear, i.e., with fastswitching graylevel patches, it performs significantly better than the optimal linear predictor.

Finite Inverted Dirichlet Mixture Optimal Pixel Predictor Image pixel prediction is a very important task in image processing. To model images, many researchers use Gaussian distributions. However, some data are obviously nonGaussian, such as the image clutter and texture. In such cases, predictors are hard to derive and to obtain. In this paper, we analytically derive a new nonlinear predictor based on inverted Dirichlet mixture. The nonlinear combination of the neighbouring pixels and the combination of the mixture parameters demonstrate a good efficiency in predicting pixels. In order to prove the efficacy of our predictor, we use two challenging tasks, which are; object detection and image restoration.