Article

Linear Asymmetric Classifier for cascade detectors

Authors:

Matthew D. Mullin,

James M. RehgAuthors Info & Claims

ICML '05: Proceedings of the 22nd international conference on Machine learning

Pages 988 - 995

https://doi.org/10.1145/1102351.1102476

Published: 07 August 2005 Publication History

Abstract

The detection of faces in images is fundamentally a rare event detection problem. Cascade classifiers provide an efficient computational solution, by leveraging the asymmetry in the distribution of faces vs. non-faces. Training a cascade classifier in turn requires a solution for the following subproblems: Design a classifier for each node in the cascade with very high detection rate but only moderate false positive rate. While there are a few strategies in the literature for indirectly addressing this asymmetric node learning goal, none of them are based on a satisfactory theoretical framework. We present a mathematical characterization of the node-learning problem and describe an effective closed form approximation to the optimal solution, which we call the Linear Asymmetric Classifier (LAC). We first use AdaBoost or AsymBoost to select features, and use LAC to learn a linear discriminant function to achieve the node learning goal. Experimental results on face detection show that LAC can improve the detection performance in comparison to standard methods. We also show that Fisher Discriminant Analysis on the features selected by AdaBoost yields better performance than AdaBoost itself.

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Cited By

Moraes DWainer JRocha A(2016)Low false positive learning with support vector machinesJournal of Visual Communication and Image Representation10.1016/j.jvcir.2016.03.00738:C(340-350)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.1016/j.jvcir.2016.03.007
Padmanabhuni BSubramanian KSundaram S(2015)Extended Metacognitive Neuro-Fuzzy Inference System for Biometric IdentificationRecent Advances in Computational Intelligence in Defense and Security10.1007/978-3-319-26450-9_12(309-338)Online publication date: 20-Dec-2015
https://doi.org/10.1007/978-3-319-26450-9_12
Paisitkriangkrai SShen Cvan den Hengel A(2014)Asymmetric Pruning for Learning Cascade DetectorsIEEE Transactions on Multimedia10.1109/TMM.2014.230872316:5(1254-1267)Online publication date: Aug-2014
https://doi.org/10.1109/TMM.2014.2308723
Show More Cited By

Linear Asymmetric Classifier for cascade detectors
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
  2. Machine learning

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Published In

cover image ACM Other conferences

ICML '05: Proceedings of the 22nd international conference on Machine learning

August 2005

1113 pages

ISBN:1595931805

DOI:10.1145/1102351

General Chair:
Saso Dzeroski
Jozef Stefan Institute, Slovenia
,
Program Chairs:
Luc De Raedt,
Stefan Wrobel

Copyright © 2005 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 August 2005

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Cited By

Moraes DWainer JRocha A(2016)Low false positive learning with support vector machinesJournal of Visual Communication and Image Representation10.1016/j.jvcir.2016.03.00738:C(340-350)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.1016/j.jvcir.2016.03.007
Padmanabhuni BSubramanian KSundaram S(2015)Extended Metacognitive Neuro-Fuzzy Inference System for Biometric IdentificationRecent Advances in Computational Intelligence in Defense and Security10.1007/978-3-319-26450-9_12(309-338)Online publication date: 20-Dec-2015
https://doi.org/10.1007/978-3-319-26450-9_12
Paisitkriangkrai SShen Cvan den Hengel A(2014)Asymmetric Pruning for Learning Cascade DetectorsIEEE Transactions on Multimedia10.1109/TMM.2014.230872316:5(1254-1267)Online publication date: Aug-2014
https://doi.org/10.1109/TMM.2014.2308723
Overett GTychsen-Smith LPetersson LPettersson NAndersson L(2014)Creating robust high-throughput traffic sign detectors using centre-surround HOG statisticsMachine Vision and Applications10.1007/s00138-011-0393-125:3(713-726)Online publication date: 1-Apr-2014
https://dl.acm.org/doi/10.1007/s00138-011-0393-1
Wu SLin KChien HChen CChen M(2013)On Generalizable Low False-Positive Learning Using Asymmetric Support Vector MachinesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2012.4625:5(1083-1096)Online publication date: 1-May-2013
https://dl.acm.org/doi/10.1109/TKDE.2012.46
Wen JXiong YWang S(2013)A novel two-stage weak classifier selection approach for adaptive boosting for cascade face detectorNeurocomputing10.1016/j.neucom.2011.12.060116(122-135)Online publication date: Sep-2013
https://doi.org/10.1016/j.neucom.2011.12.060
Gualdi GPrati ACucchiara R(2012)Multistage Particle Windows for Fast and Accurate Object DetectionIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2011.24734:8(1589-1604)Online publication date: 1-Aug-2012
https://dl.acm.org/doi/10.1109/TPAMI.2011.247
Wu JRehg J(2012)Object DetectionEnsemble Machine Learning10.1007/978-1-4419-9326-7_8(225-250)Online publication date: 19-Jan-2012
https://doi.org/10.1007/978-1-4419-9326-7_8
El Ayadi MKamel MKarray F(2011)Survey on speech emotion recognitionPattern Recognition10.1016/j.patcog.2010.09.02044:3(572-587)Online publication date: 1-Mar-2011
https://dl.acm.org/doi/10.1016/j.patcog.2010.09.020
Wen JXiong Y(2011)Asymmetric constraint optimization based adaptive boosting for cascade face detectorProceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence10.1007/978-3-642-25944-9_30(226-234)Online publication date: 11-Aug-2011
https://dl.acm.org/doi/10.1007/978-3-642-25944-9_30
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