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Resource-adaptive semantic concept detection using ensemble classifiers

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.