Learning Near Duplicate Image Pairs using Convolutional Neural Networks

doi:10.23940/ijpe.18.01.p18.168177

Abstract

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.

Submitted on November 28, 2017; Revised on December 15, 2017; Accepted on December 23, 2017
References: 33

Yi Zhang, Yanning Zhang, Jinqiu Sun, Haisen Li, and Yu Zhu. Learning Near Duplicate Image Pairs using Convolutional Neural Networks [J]. Int J Performability Eng, 2018, 14(1): 168-177.

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