NDLI: Learning Vocabularies over a Fine Quantization

Content Provider	Springer Nature Link
Author	Mikulik, Andrej Perdoch, Michal Chum, Ondřej Matas, Jiří
Copyright Year	2012
Abstract	A novel similarity measure for bag-of-words type large scale image retrieval is presented. The similarity function is learned in an unsupervised manner, requires no extra space over the standard bag-of-words method and is more discriminative than both L2-based soft assignment and Hamming embedding. The novel similarity function achieves mean average precision that is superior to any result published in the literature on the standard Oxford 5k, Oxford 105k and Paris datasets/protocols. We study the effect of a fine quantization and very large vocabularies (up to 64 million words) and show that the performance of specific object retrieval increases with the size of the vocabulary. This observation is in contradiction with previously published results. We further demonstrate that the large vocabularies increase the speed of the tf-idf scoring step.
Starting Page	163
Ending Page	175
Page Count	13
File Format	PDF
ISSN	09205691
Journal	International Journal of Computer Vision
Volume Number	103
Issue Number	1
e-ISSN	15731405
Language	English
Publisher	Springer US
Publisher Date	2012-12-08
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Image retrieval Vocabulary Feature track Computer Imaging, Vision, Pattern Recognition and Graphics Artificial Intelligence (incl. Robotics) Image Processing and Computer Vision Pattern Recognition
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence 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

Erratum to: Learning Vocabularies over a Fine Quantization

Boosting Image Retrieval

Feature-Based Image Analysis

Parametric Feature Detection

Foreground Focus: Unsupervised Learning from Partially Matching Images

Coarse-to-Fine Face Detection

Learning Low-Level Vision

Learning from User Behavior in Image Retrieval: Application of Market Basket Analysis

Local Alignments for Fine-Grained Categorization

Learning Vocabularies over a Fine Quantization

Similar Documents

Erratum to: Learning Vocabularies over a Fine Quantization

Boosting Image Retrieval

Feature-Based Image Analysis

Parametric Feature Detection

Foreground Focus: Unsupervised Learning from Partially Matching Images

Coarse-to-Fine Face Detection

Learning Low-Level Vision

Learning from User Behavior in Image Retrieval: Application of Market Basket Analysis

Local Alignments for Fine-Grained Categorization

Learning Vocabularies over a Fine Quantization