Sparse representation, which uses dictionary atoms to reconstruct input vectors, has been studied intensively in recent years. A proper dictionary is a key for the success of sparse representation. In this paper, an active dictionary learning (ADL) method is introduced, in which classification error and reconstruction error are considered as the active learning criteria in selection of the atom...

this paper presents a new three-dimensional curvelet transform based dictionary learning for automatic segmentation of intraretinalcysts, most relevant prognostic biomarker in neovascular age-related macular degeneration, from 3d spectral-domain optical coherencetomography (sd-oct) images. in particular, we focus on the spectralis sd-oct (heidelberg engineering, heidelberg, germany) system, and...

We study Dictionary Learning (aka sparse coding). By geometrically interpreting an exact formulation of Dictionary Learning, we identify related problems and draw formal relationships among them. Dictionary Learning is viewed as the minimum generating set of a subspace arrangement. This formulation leads to a new family of dictionary learning algorithms. When the data are sufficiently general a...