Visual recognition and inference using dynamic overcomplete sparse learning.
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.
Murray
Joseph F
JF
Massachusetts Institute of Technology, Brain and Cognitive Sciences Department, Cambridge, MA 02139, USA. murrayjf@mit.edu
Kreutz-Delgado
Kenneth
K
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