NDLI: Compressed Hashing

Content Provider	IEEE Xplore Digital Library
Author	Yue Lin Rong Jin Deng Cai Shuicheng Yan Xuelong Li
Copyright Year	2013
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Michigan State Univ., East Lansing, MI, USA (Rong Jin) \|\| Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore, Singapore (Shuicheng Yan) \|\| State Key Lab. of CAD&CG, Zhejiang Univ., Hangzhou, China (Yue Lin; Deng Cai) \|\| Opt. IMagery Anal. & Learning, China (Xuelong Li)
Abstract	Recent studies have shown that hashing methods are effective for high dimensional nearest neighbor search. A common problem shared by many existing hashing methods is that in order to achieve a satisfied performance, a large number of hash tables (i.e., long code-words) are required. To address this challenge, in this paper we propose a novel approach called Compressed Hashing by exploring the techniques of sparse coding and compressed sensing. In particular, we introduce as parse coding scheme, based on the approximation theory of integral operator, that generate sparse representation for high dimensional vectors. We then project s-parse codes into a low dimensional space by effectively exploring the Restricted Isometry Property (RIP), a key property in compressed sensing theory. Both of the theoretical analysis and the empirical studies on two large data sets show that the proposed approach is more effective than the state-of-the-art hashing algorithms.
Starting Page	446
Ending Page	451
File Size	371145
Page Count	6
File Format	PDF
ISBN	9780769549897
ISSN	10636919
DOI	10.1109/CVPR.2013.64
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vectors Encoding Kernel Databases Approximation algorithms Training Educational institutions Compressed Sensing Hashing Nearest Neighbor Search Random Projection
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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A Pyramid Nearest Neighbor Search Kernel for object categorization

Local Naive Bayes Nearest Neighbor for image classification

A fast nearest neighbor search algorithm by nonlinear embedding

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