Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Rate-based query optimization for streaming information sources
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Chain: operator scheduling for memory minimization in data stream systems
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Adaptive filters for continuous queries over distributed data streams
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Supporting timeliness and accuracy in distributed real-time content-based video analysis
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
On the detection of semantic concepts at TRECVID
Proceedings of the 12th annual ACM international conference on Multimedia
Multimodal Video Indexing: A Review of the State-of-the-art
Multimedia Tools and Applications
Operator scheduling in data stream systems
The VLDB Journal — The International Journal on Very Large Data Bases
Dynamic Load Distribution in the Borealis Stream Processor
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Fault-tolerance in the Borealis distributed stream processing system
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Improving classifier utility by altering the misclassification cost ratio
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
Detecting spam web pages through content analysis
Proceedings of the 15th international conference on World Wide Web
A detector tree of boosted classifiers for real-time object detection and tracking
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Large-Scale Concept Ontology for Multimedia
IEEE MultiMedia
Model-shared subspace boosting for multi-label classification
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Monitoring streams: a new class of data management applications
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
A review of text and image retrieval approaches for broadcast news video
Information Retrieval
Staying FIT: efficient load shedding techniques for distributed stream processing
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
SPADE: the system s declarative stream processing engine
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Resource-adaptive semantic concept detection using ensemble classifiers
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Resource-adaptive multimedia analysis on stream mining systems
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Exploiting multi-level parallelism for low-latency activity recognition in streaming video
MMSys '10 Proceedings of the first annual ACM SIGMM conference on Multimedia systems
Design principles for developing stream processing applications
Software—Practice & Experience - Focus on Selected PhD Literature Reviews in the Practical Aspects of Software Technology
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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.