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VIREO @ Video Browser Showdown 2019

  • Phuong Anh Nguyen
  • Chong-Wah NgoEmail author
  • Danny Francis
  • Benoit Huet
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11296)

Abstract

In this paper, the VIREO team video retrieval tool is described in details. As learned from Video Browser Showdown (VBS) 2018, the visualization of video frames is a critical need to improve the browsing effectiveness. Based on this observation, a hierarchical structure that represents the video frame clusters has been built automatically using k-means and self-organizing-map and used for visualization. Also, the relevance feedback module which relies on real-time support-vector-machine classification becomes unfeasible with the large dataset provided in VBS 2019 and has been replaced by a browsing module with pre-calculated nearest neighbors. The preliminary user study results on IACC.3 dataset show that these modules are able to improve the retrieval accuracy and efficiency in real-time video search system.

Keywords

Video visualization Video retrieval Video browser showdown 

Notes

Acknowledgments

The work described in this paper was supported by a grant from the Research Grants Council of the Hong Kong SAR, China (Reference No.: CityU 11250716), and a grant from the PROCORE-France/Hong Kong Joint Research Scheme sponsored by the Research Grants Council of Hong Kong and the Consulate General of France in Hong Kong (Reference No.: F-CityU104/17).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Phuong Anh Nguyen
    • 1
  • Chong-Wah Ngo
    • 1
    Email author
  • Danny Francis
    • 2
  • Benoit Huet
    • 2
  1. 1.Computer Science DepartmentCity University of Hong KongHong KongChina
  2. 2.Data Science Department, EURECOMBiotFrance

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