A survey on image and video cosegmentation: Methods, challenges and analyses

Yan Ren, Adams Wai-Kin Kong, Licheng Jiao

Abstract: Highlights • To the best of our knowledge, this paper is the first comprehensive survey covering both image and video cosegmentation. • To clarify the essence of cosegmentation methods, typical methodologies and formulations are summarized and outlined from a wide range of existing methods. • Through unveiling the relevance as well as the difference between the two co-related research areas, common ideas and challenges are identified and summarized for future study. 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.