{"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/iterative-projection-and-matching-finding","title":"Iterative Projection and Matching: Finding Structure-preserving Representatives and Its Application to Computer Vision","arxiv_id":"1811.12326","date":"2018-11-29","proceeding":"CVPR 2019 6","authors":["Mohsen Joneidi","Alireza Zaeemzadeh","Nazanin Rahnavard","Mubarak Shah"],"abstract":"The goal of data selection is to capture the most structural information from\na set of data. This paper presents a fast and accurate data selection method,\nin which the selected samples are optimized to span the subspace of all data.\nWe propose a new selection algorithm, referred to as iterative projection and\nmatching (IPM), with linear complexity w.r.t. the number of data, and without\nany parameter to be tuned. In our algorithm, at each iteration, the maximum\ninformation from the structure of the data is captured by one selected sample,\nand the captured information is neglected in the next iterations by projection\non the null-space of previously selected samples. The computational efficiency\nand the selection accuracy of our proposed algorithm outperform those of the\nconventional methods. Furthermore, the superiority of the proposed algorithm is\nshown on active learning for video action recognition dataset on UCF-101;\nlearning using representatives on ImageNet; training a generative adversarial\nnetwork (GAN) to generate multi-view images from a single-view input on CMU\nMulti-PIE dataset; and video summarization on UTE Egocentric dataset.","url_abs":"http://arxiv.org/abs/1811.12326v1","url_pdf":"http://arxiv.org/pdf/1811.12326v1.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":"iterative-projection-and-matching-finding","repo_url":"https://github.com/zaeemzadeh/Active-Learning-UCF101-IPM","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"iterative-projection-and-matching-finding","repo_url":"https://github.com/zaeemzadeh/IPM","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"action-recognition-in-videos","task_name":"Action Recognition"},{"task_slug":"active-learning","task_name":"Active Learning"},{"task_slug":"computational-efficiency","task_name":"Computational Efficiency"},{"task_slug":"data-summarization","task_name":"Data Summarization"},{"task_slug":null,"task_name":"Generative Adversarial Network"},{"task_slug":"action-recognition","task_name":"Temporal Action Localization"},{"task_slug":"video-summarization","task_name":"Video Summarization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}