Boosting Classifier Cascades

Mohammad J. Saberian, Nuno Vasconcelos
2010 Neural Information Processing Systems  
The problem of optimal and automatic design of a detector cascade is considered. A novel mathematical model is introduced for a cascaded detector. This model is analytically tractable, leads to recursive computation, and accounts for both classification and complexity. A boosting algorithm, FCBoost, is proposed for fully automated cascade design. It exploits the new cascade model, minimizes a Lagrangian cost that accounts for both classification risk and complexity. It searches the space of
more » ... ade configurations to automatically determine the optimal number of stages and their predictors, and is compatible with bootstrapping of negative examples and cost sensitive learning. Experiments show that the resulting cascades have state-of-the-art performance in various computer vision problems.
dblp:conf/nips/SaberianV10 fatcat:cokkzfqj75hdfdea2ftnma4quy