On Asymmetric Classifier Training for Detector Cascades

Autor:	Timothy F. Gee
Rok vydání:	2006
Předmět:	business.industry Computer science Supervised learning Pattern recognition A-weighting Facial recognition system Weighting ComputingMethodologies_PATTERNRECOGNITION AdaBoost Artificial intelligence business Face detection Classifier (UML) LogitBoost
Zdroj:	Advances in Visual Computing ISBN: 9783540486268 ISVC (2)
DOI:	10.1007/11919629_84
Popis:	This paper examines the Asymmetric AdaBoost algorithm introduced by Viola and Jones for cascaded face detection. The Viola and Jones face detector uses cascaded classifiers to successively filter, or reject, non-faces. In this approach most non-faces are easily rejected by the earlier classifiers in the cascade, thus reducing the overall number of computations. This requires earlier cascade classifiers to very seldomly reject true instances of faces. To reflect this training goal, Viola and Jones introduce a weighting parameter for AdaBoost iterations and show it enforces a desirable bound. During their implementation, a modification to the proposed weighting was introduced, while enforcing the same bound. The goal of this paper is to examine their asymmetric weighting by putting AdaBoost in the form of Additive Regression as was done by Friedman, Hastie, and Tibshirani. The author believes this helps to explain the approach and adds another connection between AdaBoost and Additive Regression.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::eabe966819daf5515bb8471c28619e60 https://doi.org/10.1007/11919629_84 Zobrazit plný text záznamu