Configuring topologies of distributed semantic concept classifiers for continuous multimedia stream processing

Real-time multimedia semantic concept detection requires instant identification of a set of concepts in streaming video or images. However, the potentially high data volumes of multimedia content, and high complexity associated with individual concept detectors, have hindered its practical deployment. In this paper, we present a new online concept detection system deployed on top of a distributed stream mining system. It uses a tree-topology of classifiers that are constructed on a semantic hierarchy of concepts of interest. We introduce a novel methodology for configuring such cascaded classifier topologies under constraints on the available resources. In our approach, we configure individual classifiers with optimized operating points after jointly and explicitly considering the misclassification cost of each end-to-end class of interest in the tree, the system imposed resource constraints, and the confidence level of each object that is classified. We describe the implemented application, system, and optimization algorithms, and verify that significant improvement in terms of accuracy of classification can be achieved through our approach.