Knapsack problems: algorithms and computer implementations
Knapsack problems: algorithms and computer implementations
Machine Learning
Adaptive filters for continuous queries over distributed data streams
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Operator scheduling in data stream systems
The VLDB Journal — The International Journal on Very Large Data Bases
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
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
MM '08 Proceedings of the 16th ACM international conference on Multimedia
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We propose a new approach for resource-adaptive semantic concept detection on image streams. We build concept detectors using an ensemble learning method called random subspace bagging, and deploy them on a set of distributed processing nodes. We focus on the optimal placement of ensemble classifiers across nodes, and selection of the number of base models for each classifier, to maximize classification performance while adapting to resource constraints. Based on a utility metric defined in terms of misclassification probabilities, we formulate this resource adaptation problem using two approaches. The first corresponds to a Multiple-Choice-Multiple-Knapsack problem solved by integer programming, while the second involves formulation as a load-balancing problem solved by linear programming. The performance of these approaches is evaluated on an application that detects 10 semantic concepts on real image streams. We show that the load-balancing approach outperforms the knapsack approach, with over 60% reduction in misclassification penalty under tight resource constraints.