{"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/sr-clustering-semantic-regularized-clustering","title":"SR-Clustering: Semantic Regularized Clustering for Egocentric Photo Streams Segmentation","arxiv_id":"1512.07143","date":"2015-12-22","proceeding":null,"authors":["Mariella Dimiccoli","Marc Bolaños","Estefania Talavera","Maedeh Aghaei","Stavri G. Nikolov","Petia Radeva"],"abstract":"While wearable cameras are becoming increasingly popular, locating relevant\ninformation in large unstructured collections of egocentric images is still a\ntedious and time consuming processes. This paper addresses the problem of\norganizing egocentric photo streams acquired by a wearable camera into\nsemantically meaningful segments. First, contextual and semantic information is\nextracted for each image by employing a Convolutional Neural Networks approach.\nLater, by integrating language processing, a vocabulary of concepts is defined\nin a semantic space. Finally, by exploiting the temporal coherence in photo\nstreams, images which share contextual and semantic attributes are grouped\ntogether. The resulting temporal segmentation is particularly suited for\nfurther analysis, ranging from activity and event recognition to semantic\nindexing and summarization. Experiments over egocentric sets of nearly 17,000\nimages, show that the proposed approach outperforms state-of-the-art methods.","url_abs":"http://arxiv.org/abs/1512.07143v2","url_pdf":"http://arxiv.org/pdf/1512.07143v2.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":"sr-clustering-semantic-regularized-clustering","repo_url":"https://github.com/MarcBS/SR-Clustering","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"}],"methods":[],"datasets_introduced":[{"slug":"edub-seg","name":"EDUB-Seg","full_name":"Egocentric Dataset of the University of Barcelona – Segmentation"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}