Near duplicate image pairs detection using double-channel convolutional neural networks

Yi Zhang, Yanning Zhang, Jinqiu Sun, Haisen Li, Yu Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Measuring the image pair similarity is a fundamental task in computer vision. This paper illustrates a neural network model to accomplish the task and decide if the input pair is a near duplicate pair. Authors explore several convolutional neural networks and adopt the double-channel network on this task. The model achieves comparable results on benchmark datasets and well performs on the closely similar images pairs among them. Comparing with the conventional approaches, the network provides a straightforward function to measure the pair-wise similarity and utilizes the strong correlation meanwhile.

Original languageEnglish
Title of host publicationProceedings - 2017 International Conference on Virtual Reality and Visualization, ICVRV 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages219-222
Number of pages4
ISBN (Electronic)9781538626368
DOIs
StatePublished - 2 Jul 2017
Event7th International Conference on Virtual Reality and Visualization, ICVRV 2017 - Zhengzhou, China
Duration: 21 Oct 201722 Oct 2017

Publication series

NameProceedings - 2017 International Conference on Virtual Reality and Visualization, ICVRV 2017

Conference

Conference7th International Conference on Virtual Reality and Visualization, ICVRV 2017
Country/TerritoryChina
CityZhengzhou
Period21/10/1722/10/17

Keywords

  • CNN
  • Jointly Processing
  • Similarity Function

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