NDLI: Sparse kernel approximations for efficient classification and detection

Content Provider	IEEE Xplore Digital Library
Author	Vedaldi, A. Zisserman, A.
Copyright Year	2012
Description	Author affiliation: Dept. of Engineering Science, University of Oxford, UK (Vedaldi, A.; Zisserman, A.)
Abstract	Efficient learning with non-linear kernels is often based on extracting features from the data that “linearise” the kernel. While most constructions aim at obtaining low-dimensional and dense features, in this work we explore high-dimensional and sparse ones. We give a method to compute sparse features for arbitrary kernels, re-deriving as a special case a popular map for the intersection kernel and extending it to arbitrary additive kernels. We show that bundle optimisation methods can handle efficiently these sparse features in learning. As an application, we show that product quantisation can be interpreted as a sparse feature encoding, and use this to significantly accelerate learning with this technique. We demonstrate these ideas on image classification with Fisher kernels and object detection with deformable part models on the challenging PASCAL VOC data, obtaining five to ten-fold speed-ups as well as reducing memory use by an order of magnitude.
Starting Page	2320
Ending Page	2327
File Size	197082
Page Count	8
File Format	PDF
ISBN	9781467312264
ISSN	10636919
e-ISBN	9781467312288
e-ISBN	9781467312271
DOI	10.1109/CVPR.2012.6247943
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	Kernel Approximation methods Vectors Encoding Support vector machines Additives Quantization
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

Efficient sparse least squares support vector machines for pattern classification

Additive Quantization for Extreme Vector Compression

Power mean SVM for large scale visual classification

Classification using intersection kernel support vector machines is efficient

A Max-Margin Perspective on Sparse Representation-Based Classification

Asymmetric Sparse Kernel Approximations for Large-Scale Visual Search

Hash-SVM: Scalable Kernel Machines for Large-Scale Visual Classification

Sparse Quantization for Patch Description

Efficient Classification for Additive Kernel SVMs

Sparse kernel approximations for efficient classification and detection

Similar Documents

Efficient sparse least squares support vector machines for pattern classification

Additive Quantization for Extreme Vector Compression

Power mean SVM for large scale visual classification

Classification using intersection kernel support vector machines is efficient

A Max-Margin Perspective on Sparse Representation-Based Classification

Asymmetric Sparse Kernel Approximations for Large-Scale Visual Search

Hash-SVM: Scalable Kernel Machines for Large-Scale Visual Classification

Sparse Quantization for Patch Description

Efficient Classification for Additive Kernel SVMs

Sparse kernel approximations for efficient classification and detection