Mixture Models for Image Representation

We consider the estimation of local grey level image structure in terms of a lay ered representation This type of representation has recently been successfully used to segment various objects from clutter using either optical ow or stereo disparity infor mation We argue that the same type of representation is useful for grey level data in that it allows for the estimation of properties for each of several di erent components without prior segmentation Our emphasis in this paper is on the process used to extract such a layered representation from a given image In particular we consider a variant of the EM algorithm for the estimation of the layered model and consider a novel technique for choosing the number of layers to use We brie y consider the use of a simple version of this approach for image segmentation and suggest two potential applications to the ARK project Category Image representation

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