Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/67358
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Type: Conference paper
Title: LACBoost and FisherBoost: optimally building cascade classifiers
Author: Shen, C.
Wang, P.
Li, H.
Citation: Proceedings of 11th European Conference on Computer Vision ( ECCV'10), 5-11 September, 2010, Part II / K. Daniilidis, P. Maragos and N. Paragios (eds.); pp. 608-621
Publisher: Springer Berlin Heidelberg
Publisher Place: NewYork
Issue Date: 2010
Series/Report no.: Lecture Notes in Computer Science; 6312
ISBN: 9783642155512
Conference Name: European Conference on Computer Vision (ECCV) (10th : 2010 : Crete, Greece)
Statement of
Responsibility: 
Chunhua Shen, Peng Wang and Hanxi Li
Abstract: Object detection is one of the key tasks in computer vision. The cascade framework of Viola and Jones has become the de facto standard. A classifier in each node of the cascade is required to achieve extremely high detection rates, instead of low overall classification error. Although there are a few reported methods addressing this requirement in the context of object detection, there is no a principled feature selection method that explicitly takes into account this asymmetric node learning objective. We provide such a boosting algorithm in this work. It is inspired by the linear asymmetric classifier (LAC) of [1] in that our boosting algorithm optimizes a similar cost function. The new totallycorrective boosting algorithm is implemented by the column generation technique in convex optimization. Experimental results on face detection suggest that our proposed boosting algorithms can improve the state-ofthe-art methods in detection performance.
Rights: © Springer-Verlag Berlin
RMID: 0020112819
DOI: 10.1007/978-3-642-15552-9
Appears in Collections:Computer Science publications

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