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Binary feature representation learning for scene retrieval in micro-video

  • Jie Guo
  • Xiushan Nie
  • Muwei Jian
  • Yilong YinEmail author
Article

Abstract

Micro-video is popular as new social media, and scene retrieval is a useful application in micro-video. At present, few researches focus on scene retrieval in micro-video, and there is a big gap between scene feature and semantics. In order to extract better semantical feature, we propose a combinational fusion method which combines multi-layer neural network and supervised hash learning method. As nonlinear projection, multi-layer neural network fuses multiple modalities by nonlinear transformation, and supervised hash learning method transforms fusion feature by linear projection to binary code for semantics and similarity preservation. We evaluate the proposed method on an actual micro-video dataset crawled from Vine. The experimental results show its superior performance than single multi-modal fusion methods and single hash learning methods.

Keywords

Scene retrieval Micro-video Multi-layer neural network Supervised hash learning 

Notes

Acknowledgements

This work is supported by the National Natural Science Foundation of China (61671274, 61573219, 61876098), China Postdoctoral Science Foundation (2016M592190), Shandong Provincial Key Research and Development Plan (2017CXGC1504), Shandong Provincial High College Science and Technology Plan (J17KB161) and the Fostering Project of Dominant Discipline and Talent Team of Shandong Province Higher Education Institutions.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyShandong UniversityJinanChina
  2. 2.Shandong University of Finance and EconomicsJinanChina
  3. 3.School of SoftwareShandong UniversityJinanChina

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