page_title: Object co-segmentation
section_title: Dynamic Markov networks-based methods
hierarchical_section_title: Object co-segmentation / Dynamic Markov networks-based methods
caption_attribution_description: English: Illustration of the Framework for Joint Video Object Discovery and Segmentation
context_page_description: In computer vision, object co-segmentation is a special case of image segmentation, which is defined as jointly segmenting semantically similar objects in multiple images or video frames.
context_section_description: A joint object discover and co-segmentation method based on coupled dynamic Markov networks has been proposed recently, which claims significant improvements in robustness against irrelevant/noisy video frames. 
  Unlike previous efforts which conveniently assumes the consistent presence of the target objects throughout the input video, this coupled dual dynamic Markov network based algorithm simultaneously carries out both the detection and segmentation tasks with two respective Markov networks jointly updated via belief propagation. 
  Specifically, the Markov network responsible for segmentation is initialized with superpixels and provides information for its Markov counterpart responsible for the object detection task. Conversely, the Markov network responsible for detection builds the object proposal graph with inputs including the spatio-temporal  segmentation tubes.
caption: A joint object discover and co-segmentation framework based on coupled dynamic Markov Networks[1].
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