NDLI: Classification using intersection kernel support vector machines is efficient

Content Provider	IEEE Xplore Digital Library
Author	Maji, S. Berg, A.C. Malik, J.
Copyright Year	2008
Description	Author affiliation: EECS Dept., U.C. Berkeley, Berkeley, CA (Maji, S.)
Abstract	Straightforward classification using kernelized SVMs requires evaluating the kernel for a test vector and each of the support vectors. For a class of kernels we show that one can do this much more efficiently. In particular we show that one can build histogram intersection kernel SVMs (IKSVMs) with runtime complexity of the classifier logarithmic in the number of support vectors as opposed to linear for the standard approach. We further show that by precomputing auxiliary tables we can construct an approximate classifier with constant runtime and space requirements, independent of the number of support vectors, with negligible loss in classification accuracy on various tasks. This approximation also applies to 1 - $chi^{2}$ and other kernels of similar form. We also introduce novel features based on a multi-level histograms of oriented edge energy and present experiments on various detection datasets. On the INRIA pedestrian dataset an approximate IKSVM classifier based on these features has the current best performance, with a miss rate 13% lower at $10^{-6}$ False Positive Per Window than the linear SVM detector of Dalal & Triggs. On the Daimler Chrysler pedestrian dataset IKSVM gives comparable accuracy to the best results (based on quadratic SVM), while being 15times faster. In these experiments our approximate IKSVM is up to 2000times faster than a standard implementation and requires 200times less memory. Finally we show that a 50times speedup is possible using approximate IKSVM based on spatial pyramid features on the Caltech 101 dataset with negligible loss of accuracy.
Starting Page	1
Ending Page	8
File Size	449031
Page Count	8
File Format	PDF
ISBN	9781424422425
ISSN	10636919
DOI	10.1109/CVPR.2008.4587630
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kernel Support vector machines Support vector machine classification Histograms Runtime Object detection Testing Face detection Decision trees Classification tree analysis
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

Support Kernel Machines for Object Recognition

Rotation Invariant Face Detection using Spectral Histograms and Support Vector Machines

Optimizing discrimination-efficiency tradeoff in integrating heterogeneous local features for object detection

Fast Support Vector Machine Classification using linear SVMs

Multi-class classification using support vector machines in decision tree architecture

Learning image similarity from Flickr groups using Stochastic Intersection Kernel MAchines

A discriminative feature space for detecting and recognizing faces

Face recognition based on Detail Histogram Intersection kernel

Face recognition based on Gray Scale Kernel

Classification using intersection kernel support vector machines is efficient

Similar Documents

Support Kernel Machines for Object Recognition

Rotation Invariant Face Detection using Spectral Histograms and Support Vector Machines

Optimizing discrimination-efficiency tradeoff in integrating heterogeneous local features for object detection

Fast Support Vector Machine Classification using linear SVMs

Multi-class classification using support vector machines in decision tree architecture

Learning image similarity from Flickr groups using Stochastic Intersection Kernel MAchines

A discriminative feature space for detecting and recognizing faces

Face recognition based on Detail Histogram Intersection kernel

Face recognition based on Gray Scale Kernel

Classification using intersection kernel support vector machines is efficient