Biologically Inspired Dictionary Learning for Visual Pattern Recognition

Holonomic brain theory provides an understanding of neural system behaviour. It is argued that recognition of objects in mammalian brain follows a sparse representation of responses to bar-like structures. Researchers considered different scales and orientations of Gabor wavelets to form a dictionary. While previous works in the literature used greedy pursuit based methods for sparse coding, this work takes advantage of a locally competitive algorithm which calculates more regular sparse coefficients by combining the interactions of artificial neurons. Moreover, the proposed learning algorithm can be implemented in parallel processing, which makes it efficient for real-time applications. A complex-valued synergetic neural network is trained using a quantum particle swarm optimization to perform a classification test. Finally, the authors have provided an experimental real application for biological implementation of sparse dictionary learning to recognize emotion using body expression. Classification results are promising and quite comparable to the recognition rate by human response.