NDLI: Efficiently training a better visual detector with sparse eigenvectors

Content Provider	IEEE Xplore Digital Library
Author	Paisitkriangkrai, S. Chunhua Shen Jian Zhang
Copyright Year	2009
Description	Author affiliation: NICTA, Sydney, NSW, Australia (Paisitkriangkrai, S.; Chunhua Shen; Jian Zhang)
Abstract	Face detection plays an important role in many vision applications. Since Viola and Jones proposed the first real-time AdaBoost based object detection system, much effort has been spent on improving the boosting method. In this work, we first show that feature selection methods other than boosting can also be used for training an efficient object detector. In particular, we have adopted greedy sparse linear discriminant analysis (GSLDA) for its computational efficiency; and slightly better detection performance is achieved compared with. Moreover, we propose a new technique, termed boosted greedy sparse linear discriminant analysis (BGSLDA), to efficiently train object detectors. BGSLDA exploits the sample re-weighting property of boosting and the class-separability criterion of GSLDA. Experiments in the domain of highly skewed data distributions, e.g., face detection, demonstrates that classifiers trained with the proposed BGSLDA outperforms AdaBoost and its variants. This finding provides a significant opportunity to argue that Adaboost and similar approaches are not the only methods that can achieve high classification results for high dimensional data such as object detection.
Starting Page	1129
Ending Page	1136
File Size	655907
Page Count	8
File Format	PDF
ISBN	9781424439928
ISSN	10636919
DOI	10.1109/CVPR.2009.5206730
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Detectors Face detection Boosting Linear discriminant analysis Object detection Los Angeles Council Real time systems Computational efficiency Statistics Robustness
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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