title: Visual recognition and inference using dynamic overcomplete sparse learning.
abstract: We present a hierarchical architecture and learning algorithm for visual recognition and other visual inference tasks such as imagination, reconstruction of occluded images, and expectation-driven segmentation. Using properties of biological vision for guidance, we posit a stochastic generative world model and from it develop a simplified world model (SWM) based on a tractable variational approximation that is designed to enforce sparse coding. Recent developments in computational methods for learning overcomplete representations (Lewicki & Sejnowski, 2000; Teh, Welling, Osindero, & Hinton, 2003) suggest that overcompleteness can be useful for visual tasks, and we use an overcomplete dictionary learning algorithm (Kreutz-Delgado, et al., 2003) as a preprocessing stage to produce accurate, sparse codings of images. Inference is performed by constructing a dynamic multilayer network with feedforward, feedback, and lateral connections, which is trained to approximate the SWM. Learning is done with a variant of the back-propagation-through-time algorithm, which encourages convergence to desired states within a fixed number of iterations. Vision tasks require large networks, and to make learning efficient, we take advantage of the sparsity of each layer to update only a small subset of elements in a large weight matrix at each iteration. Experiments on a set of rotated objects demonstrate various types of visual inference and show that increasing the degree of overcompleteness improves recognition performance in difficult scenes with occluded objects in clutter.
authors.affiliation: Massachusetts Institute of Technology, Brain and Cognitive Sciences Department, Cambridge, MA 02139, USA. murrayjf@mit.edu
authors.forename: Joseph F
authors.initials: JF
authors.lastname: Murray
authors.forename: Kenneth
authors.initials: K
authors.lastname: Kreutz-Delgado
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D017711:Nonlinear Dynamics; D010364:Pattern Recognition, Visual / Q000502:physiology*; D010775:Photic Stimulation