NDLI: Shrink boost for selecting multi-LBP histogram features in object detection

Content Provider	IEEE Xplore Digital Library
Author	Cher Keng Heng Yokomitsu, S. Matsumoto, Y. Tamura, H.
Copyright Year	2012
Description	Author affiliation: Panasonic System Networks (Yokomitsu, S.; Matsumoto, Y.; Tamura, H.) \|\| Panasonic Singapore Laboratories (Cher Keng Heng)
Abstract	Feature selection from sparse and high dimension features using conventional greedy based boosting gives classifiers of poor generalization. We propose a novel “shrink boost” method to address this problem. It solves a sparse regularization problem with two iterative steps. First, a “boosting” step uses weighted training samples to learn a full high dimensional classifier on all features. This avoids over fitting to few features and improves generalization. Next, a “shrinkage” step shrinks least discriminative classifier dimension to zero to remove the redundant features. In our object detection system, we use “shrink boost” to select sparse features from histograms of local binary pattern (LBP) of multiple quantization and image channels to learn classifier of additive lookup tables (LUT). Our evaluation shows that our classifier has much better generalization than those from greedy based boosting and those from SVM methods, even under limited number of train samples. On public dataset of human detection and pedestrian detection, we achieve better performance than state of the arts. On our more challenging dataset of bird detection, we show promising results.
Starting Page	3250
Ending Page	3257
File Size	522280
Page Count	8
File Format	PDF
ISBN	9781467312264
ISSN	10636919
e-ISBN	9781467312288
e-ISBN	9781467312271
DOI	10.1109/CVPR.2012.6248061
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Histograms Table lookup Quantization Boosting Training Classification algorithms Object detection
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
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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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