{"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/inner-and-inter-label-propagation-salient","title":"Inner and Inter Label Propagation: Salient Object Detection in the Wild","arxiv_id":"1505.07192","date":"2015-05-27","proceeding":null,"authors":["Hongyang Li","Huchuan Lu","Zhe Lin","Xiaohui Shen","Brian Price"],"abstract":"In this paper, we propose a novel label propagation based method for saliency\ndetection. A key observation is that saliency in an image can be estimated by\npropagating the labels extracted from the most certain background and object\nregions. For most natural images, some boundary superpixels serve as the\nbackground labels and the saliency of other superpixels are determined by\nranking their similarities to the boundary labels based on an inner propagation\nscheme. For images of complex scenes, we further deploy a 3-cue-center-biased\nobjectness measure to pick out and propagate foreground labels. A\nco-transduction algorithm is devised to fuse both boundary and objectness\nlabels based on an inter propagation scheme. The compactness criterion decides\nwhether the incorporation of objectness labels is necessary, thus greatly\nenhancing computational efficiency. Results on five benchmark datasets with\npixel-wise accurate annotations show that the proposed method achieves superior\nperformance compared with the newest state-of-the-arts in terms of different\nevaluation metrics.","url_abs":"http://arxiv.org/abs/1505.07192v1","url_pdf":"http://arxiv.org/pdf/1505.07192v1.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":"inner-and-inter-label-propagation-salient","repo_url":"https://github.com/hli2020/lps_tip15","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"inner-and-inter-label-propagation-salient","repo_url":"https://github.com/huchuanlu/15_7","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"computational-efficiency","task_name":"Computational Efficiency"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"salient-object-detection","task_name":"RGB Salient Object Detection"},{"task_slug":"saliency-detection","task_name":"Saliency Detection"},{"task_slug":"salient-object-detection-1","task_name":"Salient Object Detection"},{"task_slug":"superpixels","task_name":"Superpixels"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}