Abstract
Image and video cosegmentation is a newly emerging and rapidly progressing area, which aims at delineating common objects at pixel-level from a group of images or a set of videos. Plenty of related works have been published and implemented in varied applications, but there lacks a systematic survey on both image and video cosegmentation. This paper provides a comprehensive overview including the existing methods, applications, and challenges. Specifically, different cosegmentation problem settings are described, the formulation details of the methods are summarized and their potential applications are listed. Moreover, the benchmark datasets and standard evaluation metrics are also given; and the future directions and unsolved challenges are discussed.
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