{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/video-object-segmentation-using-supervoxel","title":"Video Object Segmentation using Supervoxel-Based Gerrymandering","arxiv_id":"1704.05165","date":"2017-04-18","proceeding":null,"authors":["Brent A. Griffin","Jason J. Corso"],"abstract":"Pixels operate locally. Superpixels have some potential to collect\ninformation across many pixels; supervoxels have more potential by implicitly\noperating across time. In this paper, we explore this well established notion\nthoroughly analyzing how supervoxels can be used in place of and in conjunction\nwith other means of aggregating information across space-time. Focusing on the\nproblem of strictly unsupervised video object segmentation, we devise a method\ncalled supervoxel gerrymandering that links masks of foregroundness and\nbackgroundness via local and non-local consensus measures. We pose and answer a\nseries of critical questions about the ability of supervoxels to adequately\nsway local voting; the questions regard type and scale of supervoxels as well\nas local versus non-local consensus, and the questions are posed in a general\nway so as to impact the broader knowledge of the use of supervoxels in video\nunderstanding. We work with the DAVIS dataset and find that our analysis yields\nan unsupervised method that outperforms all other known unsupervised methods\nand even many supervised ones.","url_abs":"http://arxiv.org/abs/1704.05165v1","url_pdf":"http://arxiv.org/pdf/1704.05165v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"video-object-segmentation-using-supervoxel","repo_url":"https://github.com/griffbr/supervoxel-gerrymandering","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"object","task_name":"Object"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"superpixels","task_name":"Superpixels"},{"task_slug":"unsupervised-video-object-segmentation","task_name":"Unsupervised Video Object Segmentation"},{"task_slug":"video-object-segmentation","task_name":"Video Object Segmentation"},{"task_slug":"video-semantic-segmentation","task_name":"Video Semantic Segmentation"},{"task_slug":"video-understanding","task_name":"Video Understanding"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}