Distributed classifier chain optimization for real-time multimedia stream mining systems

Brian Foo; Mihaela van der Schaar

doi:10.1117/12.766549

28 January 2008 Distributed classifier chain optimization for real-time multimedia stream mining systems

Brian Foo, Mihaela van der Schaar

Proceedings Volume 6820, Multimedia Content Access: Algorithms and Systems II; 68200N (2008) https://doi.org/10.1117/12.766549
Event: Electronic Imaging, 2008, San Jose, California, United States

Abstract

We consider the problem of optimally configuring classifier chains for real-time multimedia stream mining systems. Jointly maximizing the performance over several classifiers under minimal end-to-end processing delay is a difficult task due to the distributed nature of analytics (e.g. utilized models or stored data sets), where changing the filtering process at a single classifier can have an unpredictable effect on both the feature values of data arriving at classifiers further downstream, as well as the end-to-end processing delay. While the utility function can not be accurately modeled, in this paper we propose a randomized distributed algorithm that guarantees almost sure convergence to the optimal solution. We also provide results using speech data showing that the algorithm can perform well under highly dynamic environments.

Citation Download Citation

Brian Foo and Mihaela van der Schaar "Distributed classifier chain optimization for real-time multimedia stream mining systems", Proc. SPIE 6820, Multimedia Content Access: Algorithms and Systems II, 68200N (28 January 2008); https://doi.org/10.1117/12.766549

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