Abstract

We develop, analyze, and apply a specific form of mixture modeling for density estimation, within the context of image and texture processing. The technique captures much of the higher-order, nonlinear statistical relationships present among vector elements by combining aspects of kernel estimation and cluster analysis. Experimental results are presented in the following applications: image restoration, image and texture compression, and texture classification. 1 Introduction In many signal processing tasks, uncertainty plays a fundamental role. Examples of such tasks are compression, detection, estimation, classification, and restoration --- in all of these, the future inputs are not known perfectly at the time of system design, but instead must be characterized only in terms of their "typical," or "likely" behavior, by means of some probabilistic model. Every such system has a probabilistic model, be it explicit or implicit. Often, the level of performance achieved by such a syste...

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