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Learning Near Duplicate Image Pairs using Convolutional Neural Networks

Volume 14, Number 1, January 2018, pp. 168-177
DOI: 10.23940/ijpe.18.01.p18.168177

Yi Zhanga, Yanning Zhanga, Jinqiu Sunb, Haisen Lia, Yu Zhua

aSchool of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an, 710072, China
bSchool of Astronautics, Northwestern Polytechnical University, Xi’an, 710072, China

(Submitted on November 28, 2017; Revised on December 15, 2017; Accepted on December 23, 2017)

Abstract:

In this paper, we illustrate how to learn a general straightforward similarity function from raw image pairs, which is a fundamental task in computer vision. To encode the function, inspired by the recent achievements of deep learning methods, we explore several deep neural networks and adopt one of the suitable networks to our task encoding implementation with several models on benchmark datasets UKBench and Holidays. The adopted network achieves comparable overall results and especially presents the excellent learning ability for global-similar data. Compared to previous approaches, this function eliminates the complex handcrafted features extraction, and utilizes pairwise correlation information by the jointly processing.

 

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