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Fast large-scale object retrieval with binary quantization

[+] Author Affiliations
Shifu Zhou, Dan Zeng, Wei Shen, Zhijiang Zhang

Shanghai University, School of Communication and Information Engineering, No.149, Yanchang Road, Zhabei District, Shanghai 200072 China

Qi Tian

University of Texas at San Antonio, Department of Computer Science, 1 UTSA Boulevard, San Antonio, Texas 78249, United States

J. Electron. Imaging. 24(6), 063018 (Dec 18, 2015). doi:10.1117/1.JEI.24.6.063018
History: Received June 18, 2015; Accepted November 20, 2015
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Abstract.  The objective of large-scale object retrieval systems is to search for images that contain the target object in an image database. Where state-of-the-art approaches rely on global image representations to conduct searches, we consider many boxes per image as candidates to search locally in a picture. In this paper, a feature quantization algorithm called binary quantization is proposed. In binary quantization, a scale-invariant feature transform (SIFT) feature is quantized into a descriptive and discriminative bit-vector, which allows itself to adapt to the classic inverted file structure for box indexing. The inverted file, which stores the bit-vector and box ID where the SIFT feature is located inside, is compact and can be loaded into the main memory for efficient box indexing. We evaluate our approach on available object retrieval datasets. Experimental results demonstrate that the proposed approach is fast and achieves excellent search quality. Therefore, the proposed approach is an improvement over state-of-the-art approaches for object retrieval.

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Citation

Shifu Zhou ; Dan Zeng ; Wei Shen ; Zhijiang Zhang and Qi Tian
"Fast large-scale object retrieval with binary quantization", J. Electron. Imaging. 24(6), 063018 (Dec 18, 2015). ; http://dx.doi.org/10.1117/1.JEI.24.6.063018


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