NDLI: Multiple kernel learning from sets of partially matching image features

Content Provider	IEEE Xplore Digital Library
Author	Siyao Fu Guo ShengYang Zengguang Hou Zize Liang Min Tan
Copyright Year	2008
Description	Author affiliation: Key Lab. of Complex Syst. & Intell. Sci., Chinese Acad. of Sci., Beijing, China (Zengguang Hou; Zize Liang; Min Tan) \|\| Sch. of Inf. & Eng., Central Univ. of Nat., China (Siyao Fu; Guo ShengYang)
Abstract	Recent publications and developments based on SVM have shown that using multiple kernels instead of a single one can enhance interpretability of the decision function and improve classifier performance, which motivates researchers to explore the use of homogeneous model obtained as linear combinations of kernels. However, the use of multiple kernels faces the challenge of choosing the kernel weights, and an increased number of parameters that may lead to overfitting. In this paper we show that MKL problem with a enhanced spatial pyramid match kernel can be solved efficiently using projected gradient method. Weights on each kernel matrix (level) are included in the standard SVM empirical risk minimization problem with a $L_{2}$ constraint to encourage sparsity. We demonstrate our algorithm on classification tasks, which is based on a linear combination of the proposed kernels computed at multiple pyramid levels of image encoding, and we show that the proposed method is accurate and significantly more efficient than current approaches.
Starting Page	1
Ending Page	4
File Size	227667
Page Count	4
File Format	PDF
ISBN	9781424421749
ISSN	10514651
DOI	10.1109/ICPR.2008.4761342
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-08
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 Image coding Histograms Gradient methods Learning systems Computer vision Laboratories Intelligent systems
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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

The pyramid match kernel: discriminative classification with sets of image features

Support Kernel Machines for Object Recognition

Learning image similarity from Flickr groups using Stochastic Intersection Kernel MAchines

Integrating multi-features by multiple kernel learning to better classify images

Histogram intersection kernel for image classification

What is the spatial extent of an object?

Bayesian network structure learning and inference in indoor vs. outdoor image classification

Group-sensitive multiple kernel learning for object categorization

Tangent vector kernels for invariant image classification with SVMs

Multiple kernel learning from sets of partially matching image features

Similar Documents

The pyramid match kernel: discriminative classification with sets of image features

Support Kernel Machines for Object Recognition

Learning image similarity from Flickr groups using Stochastic Intersection Kernel MAchines

Integrating multi-features by multiple kernel learning to better classify images

Histogram intersection kernel for image classification

What is the spatial extent of an object?

Bayesian network structure learning and inference in indoor vs. outdoor image classification

Group-sensitive multiple kernel learning for object categorization

Tangent vector kernels for invariant image classification with SVMs

Multiple kernel learning from sets of partially matching image features