Part of Advances in Neural Information Processing Systems 15 (NIPS 2002)
Yan Karklin, Michael Lewicki
We present a hierarchical Bayesian model for learning efﬁcient codes of higher-order structure in natural images. The model, a non-linear gen- eralization of independent component analysis, replaces the standard as- sumption 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.