GPU Hierarchical Quilted Self Organizing Maps for Multimedia Understanding

Abstract

It is well established that the human brain outperforms current computers, concerning pattern recognition tasks, through the collaborative processing of simple building units (neurons). In this work we expand an abstracted model of the neocortex called Hierarchical Quilted Self Organizing Map, benefiting from the parallel power of current Graphical Processing Units, to achieve realtime understanding and classification of spatio-temporal sensory information. We also propose an improvement on the original model that allows the learning rate to be automatically adapted according to the input training data available. The overall system is tested on the task of gesture recognition from a Microsoft Kinect publicly available dataset.