{"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/feature-learning-by-multidimensional-scaling","title":"Feature Learning by Multidimensional Scaling and its Applications in Object Recognition","arxiv_id":"1306.3294","date":"2013-06-14","proceeding":null,"authors":["Quan Wang","Kim L. Boyer"],"abstract":"We present the MDS feature learning framework, in which multidimensional\nscaling (MDS) is applied on high-level pairwise image distances to learn\nfixed-length vector representations of images. The aspects of the images that\nare captured by the learned features, which we call MDS features, completely\ndepend on what kind of image distance measurement is employed. With properly\nselected semantics-sensitive image distances, the MDS features provide rich\nsemantic information about the images that is not captured by other feature\nextraction techniques. In our work, we introduce the iterated\nLevenberg-Marquardt algorithm for solving MDS, and study the MDS feature\nlearning with IMage Euclidean Distance (IMED) and Spatial Pyramid Matching\n(SPM) distance. We present experiments on both synthetic data and real images\n--- the publicly accessible UIUC car image dataset. The MDS features based on\nSPM distance achieve exceptional performance for the car recognition task.","url_abs":"http://arxiv.org/abs/1306.3294v1","url_pdf":"http://arxiv.org/pdf/1306.3294v1.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":"feature-learning-by-multidimensional-scaling","repo_url":"https://github.com/wq2012/SimpleMatrix","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"object-recognition","task_name":"Object 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}