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. We considered different scales and orie ntations 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 (LCA) which calculates more regular sparse coefficients by combining the interactions of artif icial neurons. Moreover the proposed learning algorithm can be implemented in parallel processing which makes it efficient for real-time application s. A complex-valued synergetic neural network is train ed using a quantum particle swarm optimization to perform a classification test. Finally, we provide an experimental real application for biological implementation of sparse dictionary learning to rec ognize emotion using body expression. Classificatio n results are promising and quite comparable to the r ecognition rate by human response. Povzetek: Z zgledovanjem po biolookih sistemih je p redstavljena je metoda ucenja vizualnih vzorcev.