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Concept
Object co-segmentation
Résumé
In computer vision, object co-segmentation is a special case of , which is defined as jointly segmenting semantically similar objects in multiple images or video frames.
It is often challenging to extract segmentation masks of a target/object from a noisy collection of images or video frames, which involves object discovery coupled with . A noisy collection implies that the object/target is present sporadically in a set of images or the object/target disappears intermittently throughout the video of interest. Early methods typically involve mid-level representations such as object proposals.
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.
Graph cut optimization is a popular tool in computer vision, especially in earlier applications. As an extension of regular graph cuts, multi-level hypergraph cut is proposed to account for more complex high order correspondences among video groups beyond typical pairwise correlations.
With such hypergraph extension, multiple modalities of correspondences, including low-level appearance, saliency, coherent motion and high level features such as object regions, could be seamlessly incorporated in the hyperedge computation.
Source officielle
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