NDLI: Re-ranking with two-level hashing

Content Provider	IEEE Xplore Digital Library
Author	Shao-Yong Feng Xing Tian Ng, W.W.Y.
Copyright Year	2014
Description	Author affiliation: Machine Learning & Cybern. Res. Center, South China Univ. of Technol., Guangzhou, China (Shao-Yong Feng; Xing Tian; Ng, W.W.Y.)
Abstract	As the exponential growth of the Internet database, searching nearest neighbors in a high dimensional space using linear time algorithms has become impractical. In recent years, hashing based approximated nearest neighbor search has been studied intensively and successfully applied to many fields. The major challenge of hashing researches is to trade-off the similarity preservation with the code length for storage efficiency. A long binary code is required to preserve a good similarity among images, but recall rate and efficiently decrease. A short binary code yields a high recall rate but a low precision rate. Almost all of the existing methods do not explicitly address this dilemma. In practice, most of them need to find the appropriate binary code length to balance precision, recall and computation efficiency. In this paper, we propose a re-ranking scheme that can be applied to most of existing hashing methods. We first generate a low dimensional binary code as the search index that roughly approximates the similarity. Then, a high dimensional binary code is generated to re-rank those roughly similar images. In this way, a higher precision rate is achieved while keeping the computation cost low.
Starting Page	98
Ending Page	103
File Size	5852002
Page Count	6
File Format	PDF
ISBN	9781479942121
ISSN	21585695
e-ISBN	9781479942114
DOI	10.1109/ICWAPR.2014.6961298
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-13
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Quantization (signal) Databases Hashing; Re Approximate Nearest Neighbor Search Binary codes Artificial neural networks Wavelet analysis Vectors ranking Computational efficiency
Content Type	Text
Resource Type	Article

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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
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