{"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/information-theoretic-active-learning-for","title":"Information-Theoretic Active Learning for Content-Based Image Retrieval","arxiv_id":"1809.02337","date":"2018-09-07","proceeding":null,"authors":["Björn Barz","Christoph Käding","Joachim Denzler"],"abstract":"We propose Information-Theoretic Active Learning (ITAL), a novel batch-mode\nactive learning method for binary classification, and apply it for acquiring\nmeaningful user feedback in the context of content-based image retrieval.\nInstead of combining different heuristics such as uncertainty, diversity, or\ndensity, our method is based on maximizing the mutual information between the\npredicted relevance of the images and the expected user feedback regarding the\nselected batch. We propose suitable approximations to this computationally\ndemanding problem and also integrate an explicit model of user behavior that\naccounts for possible incorrect labels and unnameable instances. Furthermore,\nour approach does not only take the structure of the data but also the expected\nmodel output change caused by the user feedback into account. In contrast to\nother methods, ITAL turns out to be highly flexible and provides\nstate-of-the-art performance across various datasets, such as MIRFLICKR and\nImageNet.","url_abs":"http://arxiv.org/abs/1809.02337v2","url_pdf":"http://arxiv.org/pdf/1809.02337v2.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":"information-theoretic-active-learning-for","repo_url":"https://github.com/cvjena/ITAL","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"active-learning","task_name":"Active Learning"},{"task_slug":"binary-classification","task_name":"Binary Classification"},{"task_slug":"content-based-image-retrieval","task_name":"Content-Based Image Retrieval"},{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"image-retrieval","task_name":"Image Retrieval"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}