• DocumentCode
    2490795
  • Title

    Experts-Shift: Learning active spatial classification experts for keyframe-based video segmentation

  • Author

    Zhao, Yibiao ; Duan, Yanbiao ; Nie, Xiaohan ; Huang, Yaping ; Luo, Siwei

  • Author_Institution
    Beijing Jiaotong Univ., Beijing, China
  • fYear
    2011
  • fDate
    5-7 Jan. 2011
  • Firstpage
    622
  • Lastpage
    627
  • Abstract
    Experts-Shift is a novel statistical framework for keyframe-based video segmentation. Compared to existing video segmentation techniques with simple color models, our method proposes a probability mixture model coupling strong image classifiers (experts) with latent spatial configuration. In order to propagate image labels to the successive frames, our algorithm track all experts jointly by a efficient MCMC sampler with their relations modeled by MRFs. This algorithm is capable to handle overlapping color distribution, ambiguous image boundaries, large displacement in challenging scenario with a solid foundation of both generative modeling and discriminative learning. Experiment shows our algorithm achieves high quality results and need less supervision than previous work.
  • Keywords
    Markov processes; Monte Carlo methods; image classification; image colour analysis; image segmentation; learning (artificial intelligence); video signal processing; MCMC sampler; color models; discriminative learning; experts-shift; generative modeling; image classifiers; keyframe-based video segmentation; learning active spatial classification experts; probability mixture model coupling; Computer vision; Image color analysis; Image segmentation; Joints; Pixel; Video sequences;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Applications of Computer Vision (WACV), 2011 IEEE Workshop on
  • Conference_Location
    Kona, HI
  • ISSN
    1550-5790
  • Print_ISBN
    978-1-4244-9496-5
  • Type

    conf

  • DOI
    10.1109/WACV.2011.5711562
  • Filename
    5711562