{"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/a-learning-based-visual-saliency-fusion-model","title":"A Learning-Based Visual Saliency Fusion Model for High Dynamic Range Video (LBVS-HDR)","arxiv_id":"1803.04827","date":"2018-03-13","proceeding":null,"authors":["Amin Banitalebi-Dehkordi","Yuanyuan Dong","Mahsa T. Pourazad","Panos Nasiopoulos"],"abstract":"Saliency prediction for Standard Dynamic Range (SDR) videos has been well\nexplored in the last decade. However, limited studies are available on High\nDynamic Range (HDR) Visual Attention Models (VAMs). Considering that the\ncharacteristic of HDR content in terms of dynamic range and color gamut is\nquite different than those of SDR content, it is essential to identify the\nimportance of different saliency attributes of HDR videos for designing a VAM\nand understand how to combine these features. To this end we propose a\nlearning-based visual saliency fusion method for HDR content (LVBS-HDR) to\ncombine various visual saliency features. In our approach various conspicuity\nmaps are extracted from HDR data, and then for fusing conspicuity maps, a\nRandom Forests algorithm is used to train a model based on the collected data\nfrom an eye-tracking experiment. Performance evaluations demonstrate the\nsuperiority of the proposed fusion method against other existing fusion\nmethods.","url_abs":"http://arxiv.org/abs/1803.04827v1","url_pdf":"http://arxiv.org/pdf/1803.04827v1.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":"a-learning-based-visual-saliency-fusion-model","repo_url":"https://gitlab.com/abanitalebi/lbvs-hdr","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"saliency-prediction","task_name":"Saliency Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}