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A Multi-Layer MRF Model for Video Object Segmentation

  • Zoltan Kato
  • Ting-Chuen Pong
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3852)

Abstract

A novel video object segmentation method is proposed which aims at combining color and motion information. The model has a multi-layer structure: Each feature has its own layer, called feature layer, where a classical Markov random field (MRF) image segmentation model is defined using only the corresponding feature. A special layer is assigned to the combined MRF model, called combined layer, which interacts with each feature layer and provides the segmentation based on the combination of different features. Unlike previous methods, our approach doesn’t assume motion boundaries being part of spatial ones. Therefore a very important property of the proposed method is the ability to detect boundaries that are visible only in the motion feature as well as those visible only in the color one. The method is validated on synthetic and real video sequences.

Keywords

Markov Random Field Feature Layer Color Layer Motion Segmentation Markov Random Field Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zoltan Kato
    • 1
  • Ting-Chuen Pong
    • 2
  1. 1.Institute of InformaticsUniversity of SzegedSzegedHungary
  2. 2.Computer Science DepartmentHong Kong University of Science and TechnologyKowloon, Hong KongChina

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