{"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/deep-filter-banks-for-texture-recognition-and","title":"Deep Filter Banks for Texture Recognition and Segmentation","arxiv_id":null,"date":"2015-06-01","proceeding":"CVPR 2015 6","authors":["Mircea Cimpoi","Subhransu Maji","Andrea Vedaldi"],"abstract":"Research in texture recognition often concentrates on the problem of material recognition in uncluttered conditions, an assumption rarely met by applications. In this work we conduct a first study of material and describable texture attributes recognition in clutter, using a new dataset derived from the OpenSurface texture repository. Motivated by the challenge posed by this problem, we propose a new texture descriptor, \\dcnn, obtained by Fisher Vector pooling of a Convolutional Neural Network (CNN) filter bank. \\dcnn substantially improves the state-of-the-art in texture, material and scene recognition. Our approach achieves 79.8\\% accuracy on Flickr material dataset and 81\\% accuracy on MIT indoor scenes, providing absolute gains of more than 10\\% over existing approaches. \\dcnn easily transfers across domains without requiring feature adaptation as for methods that build on the fully-connected layers of CNNs. Furthermore, \\dcnn can seamlessly incorporate multi-scale information and describe regions of arbitrary shapes and sizes. Our approach is particularly suited at localizing ``stuff'' categories and obtains state-of-the-art results on MSRC segmentation dataset, as well as promising results on recognizing materials and surface attributes in clutter on the OpenSurfaces dataset.","url_abs":"http://openaccess.thecvf.com/content_cvpr_2015/html/Cimpoi_Deep_Filter_Banks_2015_CVPR_paper.html","url_pdf":"http://openaccess.thecvf.com/content_cvpr_2015/papers/Cimpoi_Deep_Filter_Banks_2015_CVPR_paper.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":"deep-filter-banks-for-texture-recognition-and","repo_url":"https://github.com/mcimpoi/deep-fbanks","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"material-recognition","task_name":"Material Recognition"},{"task_slug":"scene-recognition","task_name":"Scene Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}