NDLI: Cross-Indexing of Binary SIFT Codes for Large-Scale Image Search

Content Provider	IEEE Xplore Digital Library
Author	Zhen Liu Houqiang Li Liyan Zhang Wengang Zhou Qi Tian
Copyright Year	1992
Abstract	In recent years, there has been growing interest in mapping visual features into compact binary codes for applications on large-scale image collections. Encoding high-dimensional data as compact binary codes reduces the memory cost for storage. Besides, it benefits the computational efficiency since the computation of similarity can be efficiently measured by Hamming distance. In this paper, we propose a novel flexible scale invariant feature transform (SIFT) binarization (FSB) algorithm for large-scale image search. The FSB algorithm explores the magnitude patterns of SIFT descriptor. It is unsupervised and the generated binary codes are demonstrated to be dispreserving. Besides, we propose a new searching strategy to find target features based on the cross-indexing in the binary SIFT space and original SIFT space. We evaluate our approach on two publicly released data sets. The experiments on large-scale partial duplicate image retrieval system demonstrate the effectiveness and efficiency of the proposed algorithm.
Sponsorship	IEEE Signal Processing Society
Page Count	11
File Size	4446047
Starting Page	2047
Ending Page	2057
File Format	PDF
ISSN	10577149
Volume Number	23
Issue Number	5
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Visualization Binary codes Indexing Hamming distance Vectors Feature extraction Quantization (signal) image search SIFT binarization cross indexing large scale
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Software

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