Sparse coding of natural images using a prior on edge co-occurences

Oriented edges in images commonly occur in co-linear and co-circular arrangements, obeying the "good continuation law" of Gestalt psychology. The human visual system appears to exploit this property of images, with contour detection, line completion, and grouping performance well predicted by such an "association field" between edge elements [1, 2]. In this paper, we show that an association field of this type can be used to enhance the sparse representation of natural images. First, we define the SparseLets framework as an efficient representation of images based on a discrete wavelet transform. Second, we extract second-order information about edge co-occurrences from a set of images of natural scenes. Finally, we incorporate this prior information into our framework and show that it allows for the extraction of features relevant to natural scenes, like a round shape. This novel approach points the way to practical computer vision algorithms with human-like performance.