Modeling Nonlinear Dependencies in Natural Images using Mixture of Laplacian Distribution

Part of Advances in Neural Information Processing Systems 17 (NIPS 2004)

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Hyun Park, Te Lee


Capturing dependencies in images in an unsupervised manner is important for many image processing applications. We propose a new method for capturing nonlinear dependencies in images of natural scenes. This method is an extension of the linear Independent Component Analysis (ICA) method by building a hierarchical model based on ICA and mixture of Laplacian distribution. The model parameters are learned via an EM algorithm and it can accurately capture variance correlation and other high order structures in a simple manner. We visualize the learned variance structure and demonstrate applications to image segmentation and denoising.