Object recognition using sparse representation of overcomplete dictionary

Abstract

Research in computational neuroscience via Functional magnetic resonance imaging (fMRI) argued that recognition of objects in mammalian brain follows a sparse representation of responses to bar-like structures. We considered different scales and orientations of Gabor wavelets to form a dic-tionary. While previous works in the literature used greedy pursuit based meth-ods for sparse coding, this work takes advantage of a locally competitive algo-rithm (LCA) which calculates more regular sparse coefficients by combining the interactions of artificial neurons. Moreover proposed learning algorithm can be implemented in parallel processing which makes it efficient for real-time ap-plications. A synergetic neural network is used to form a prototype template, representing general characteristic of a class. A classification experiment is performed based on multi-template matching. © 2012 Springer-Verlag.