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Signal, Image and Video Processing

, Volume 13, Issue 1, pp 9–16 | Cite as

Unsupervised video object segmentation using conditional random fields

  • Asma Hamza Bhatti
  • Anis Ur RahmanEmail author
  • Asad Anwar Butt
Original Paper
  • 142 Downloads

Abstract

In this work, we propose a graph-based superpixel segmentation technique to perform spatiotemporal oversegmentation of videos. The generated superpixels are post-processed by applying a straightforward threshold-based foreground separation model. These superpixels are used in a conditional random field, and a potential function is defined, which is solved using energy minimization techniques to produce a final segmentation. Experiments on two datasets containing over 24 videos demonstrate that our method produces competitive or better results for the video object segmentation task over the state-of-the-art algorithms.

Keywords

Segmentation Video Superpixels 

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Electrical Engineering and Computer SciencesNational University of Sciences and TechnologyIslamabadPakistan
  2. 2.National Institute of Standards and Technology (NIST)GaithersburgUSA

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