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Journal of Electronic Imaging

Fast large-scale object retrieval with binary quantization
Author(s): Shifu Zhou; Dan Zeng; Wei Shen; Zhijiang Zhang; Qi Tian
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Paper 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.

Paper Details

Date Published: 18 December 2015
PDF: 7 pages
J. Electron. Imaging. 24(6) 063018 doi: 10.1117/1.JEI.24.6.063018
Published in: Journal of Electronic Imaging Volume 24, Issue 6
Show Author Affiliations
Shifu Zhou, Shanghai Univ. (China)
Dan Zeng, Shanghai Univ. (China)
Wei Shen, Shanghai Univ. (China)
Zhijiang Zhang, Shanghai Univ. (China)
Qi Tian, The Univ. of Texas at San Antonio (United States)


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