Adaptive video fast forward

Nemanja Petrovic, Nebojsa Jojic, Thomas S Huang

Research output: Contribution to journalArticlepeer-review

Abstract

We derive a statistical graphical model of video scenes with multiple, possibly occluded objects that can be efficiently used for tasks related to video search, browsing and retrieval. The model is trained on query (target) clip selected by the user. Shot retrieval process is based on the likelihood of a video frame under generative model. Instead of using a combination of weighted Euclidean distances as a shot similarity measure, the likelihood model automatically separates and balances various causes of variability in video, including occlusion, appearance change and motion. Thus, we overcome tedious and complex user interventions required in previous studies. We use the model in the adaptive video forward application that adapts video playback speed to the likelihood of the data. The similarity measure of each candidate clip to the target clip defines the playback speed. Given a query, the video is played at a higher speed as long as video content has low likelihood, and when frames similar to the query clip start to come in, the video playback rate drops. Set of experiments o12n typical home videos demonstrate performance, easiness and utility of our application.

Original languageEnglish (US)
Pages (from-to)327-344
Number of pages18
JournalMultimedia Tools and Applications
Volume26
Issue number3
DOIs
StatePublished - Aug 1 2005

Keywords

  • Content-based retrieval
  • Generative models
  • Video fast forward

ASJC Scopus subject areas

  • Software
  • Media Technology
  • Hardware and Architecture
  • Computer Networks and Communications

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