Personalized key frame recommendation

Xu Chen, Yongfeng Zhang, Qingyao Ai, Hongteng Xu, Junchi Yan, Zheng Qiny

Research output: Chapter in Book/Report/Conference proceedingConference contribution

57 Scopus citations

Abstract

Key frames are playing a very important role for many video applications, such as on-line movie preview and video information retrieval. Although a number of key frame selection methods have been proposed in the past, existing technologies mainly focus on how to precisely summarize the video content, but seldom take the user preferences into consideration. However, in real scenarios, people may cast diverse interests on the contents even for the same video, and thus they may be attracted by quite different key frames, which makes the selection of key frames an inherently personalized process. In this paper, we propose and investigate the problem of personalized key frame recommendation to bridge the above gap. To do so, we make use of video images and user time-synchronized comments to design a novel key frame recommender that can simultaneously model visual and textual features in a unified framework. By user personalization based on her/his previously reviewed frames and posted comments, we are able to encode different user interests in a unified multi-modal space, and can thus select key frames in a personalized manner, which, to the best of our knowledge, is the first time in the research field of video content analysis. Experimental results show that our method performs better than its competitors on various measures.

Original languageEnglish (US)
Title of host publicationSIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation for Computing Machinery, Inc
Pages315-324
Number of pages10
ISBN (Electronic)9781450350228
DOIs
StatePublished - Aug 7 2017
Externally publishedYes
Event40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2017 - Tokyo, Shinjuku, Japan
Duration: Aug 7 2017Aug 11 2017

Publication series

NameSIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval

Other

Other40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2017
Country/TerritoryJapan
CityTokyo, Shinjuku
Period8/7/178/11/17

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Software
  • Computer Graphics and Computer-Aided Design

Keywords

  • Collaborative Filtering
  • Key Frame
  • Personalization
  • Recommender Systems
  • Video Content Analysis

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