We develop, analyze, and apply a speci c 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 classi cation.