Abstract: A computer-readable medium for performing target object recognition in images and video includes instructions for receiving target image data including a target object, applying non-negative matrix factorization with enforced sparseness to the target image data to generate target extracted image feature data, training a neural network to identify the target object using the target extracted image feature data to obtain a trained neural network, receiving object image data, applying non-negative matrix factorization with enforced sparseness to the object image data to generate object extracted image feature data, analyzing the object extracted image feature data with the trained neural network to obtain a result indicating whether the presence of the target object is identified in the object image data, and storing the result of analyzing the object extracted image feature data.

Abstract: A system is claimed that deploys a multi-layer neural network for analyzing, categorizing and organizing conversations and video conferencing on Voice over Internet Protocol (VoIP), The parallel architecture of this implementation is an artificial neural network which allows it to process very large amounts of voice data very efficiently, both in dealing with large, continuous streams of information in real time as well as on voice data archives This invention uses its neural network to operate considerably faster than its linear counterparts as found ill current VoIP message and call management software.