Fast large-scale object retrieval with binary quantization

Shifu Zhou; Dan Zeng; Wei Shen; Zhijiang Zhang; Qi Tian

doi:10.1117/1.JEI.24.6.063018

18 December 2015 Fast large-scale object retrieval with binary quantization

Shifu Zhou, Dan Zeng, Wei Shen, Zhijiang Zhang, Qi Tian

Author Affiliations +

Journal of Electronic Imaging, Vol. 24, Issue 6, 063018 (December 2015). https://doi.org/10.1117/1.JEI.24.6.063018

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.

Citation Download Citation

Shifu Zhou, Dan Zeng, Wei Shen, Zhijiang Zhang, and Qi Tian "Fast large-scale object retrieval with binary quantization," Journal of Electronic Imaging 24(6), 063018 (18 December 2015). https://doi.org/10.1117/1.JEI.24.6.063018

Published: 18 December 2015

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