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Proceedings Paper

Large-scale book page retrieval by deep hashing networks
Author(s): Jiao Huo; Yi Zhao; Leyuan Liu
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Paper Abstract

Nowadays, more and more printed books are accompanied by electronic resources including videos, audios, games, augmented reality and other mobile apps. However, it is not very convenient to access most of these electronic resources, as the association between printed books and electronic resources is not automatically available [2]. To build a bridge between a book page and the corresponding electronic resources, a large-scale book page retrieval method using deep hashing network is presented in this paper. There are mainly three contributions: First, a pipeline is proposed to make a Convolutional Neural Network (CNN) trained for another unrelated task available for book page retrieval. Second, the high-dimensional features extracted from the CNN is mapped to the low-dimensional binary hash code sequence in Hemming space by the deep hashing network, which not only increases the speed of retrieval but also saves the space of feature storage. Third, a large-scale dataset which is consist of 1.55M book page images is collected. Experimental results on the 1.55M book page dataset show that the proposed deep hashing network achieves a Top-1 hit rate of 92.1% and the response time is less than 0.6 second on a desktop computer with a GeForce 1080Ti GPU.

Paper Details

Date Published: 14 February 2020
PDF: 9 pages
Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 1143023 (14 February 2020); doi: 10.1117/12.2541935
Show Author Affiliations
Jiao Huo, Wuhan Zhimei Technology Co., Ltd. (China)
Yi Zhao, Central China Normal Univ. (China)
Leyuan Liu, Central China Normal Univ. (China)

Published in SPIE Proceedings Vol. 11430:
MIPPR 2019: Pattern Recognition and Computer Vision
Nong Sang; Jayaram K. Udupa; Yuehuan Wang; Zhenbing Liu, Editor(s)

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