Higher-order structure of natural images

We present a statistical model for learning efficient codes of higher-order structure in natural images. The model, a non-linear generalization of independent component analysis, replaces the standard assumption of independence for the joint distribution of coefficients with a distribution that is adapted to the variance structure of the coefficients of an efficient image basis. This offers a novel description of higher order image structure and provides a way to learn coarse-coded, sparse-distributed representations of abstract image properties such as object location, scale, and texture.

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