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Deep regional feature pooling for video matching

  • Yan Bai
  • , Jie Lin
  • , Vijay Chandrasekhar
  • , Yihang Lou
  • , Shiqi Wang
  • , Ling Yu Duan*
  • , Tiejun Huang
  • , Alex Kot
  • *Corresponding author for this work
  • Peking University
  • Agency for Science, Technology and Research, Singapore
  • Nanyang Technological University
  • City University of Hong Kong

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

Abstract

In this work, we study the problem of deep global descriptors for video matching with regional feature pooling. We aim to analyze the joint effect of ROI (Region of Interest) size and pooling moment on video matching performance. To this end, we propose to mathematically model the distribution of video matching function with a pooling function nested in. Matching performance can be estimated by the separability of these class-conditional distributions between matching and non-matching pairs. Empirical studies on the challenging MPEG CDVA dataset demonstrate that performance trends are consistent with the estimation and experimental results, though the theoretical model is largely simplified compared to video matching and retrieval in practice.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings
PublisherIEEE Computer Society
Pages380-384
Number of pages5
ISBN (Electronic)9781509021758
DOIs
StatePublished - 2 Jul 2017
Externally publishedYes
Event24th IEEE International Conference on Image Processing, ICIP 2017 - Beijing, China
Duration: 17 Sep 201720 Sep 2017

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2017-September
ISSN (Print)1522-4880

Conference

Conference24th IEEE International Conference on Image Processing, ICIP 2017
Country/TerritoryChina
CityBeijing
Period17/09/1720/09/17

Keywords

  • Convolutional Neural Networks
  • Global Descriptor
  • Pooling
  • Video Matching
  • Video Retrieval

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