{"id":153262,"date":"2008-06-01T00:00:00","date_gmt":"2008-06-01T00:00:00","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/msr-research-item\/online-multi-label-active-learning-for-large-scale-multimedia-annotation\/"},"modified":"2018-10-16T21:19:03","modified_gmt":"2018-10-17T04:19:03","slug":"online-multi-label-active-learning-for-large-scale-multimedia-annotation","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/online-multi-label-active-learning-for-large-scale-multimedia-annotation\/","title":{"rendered":"Online Multi-Label Active Learning for Large-Scale Multimedia Annotation"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Existing video search engines have not taken the advantages of video content analysis and semantic understanding. Video search in academia uses semantic annotation to approach content-based indexing. We argue this is a promising direction to enable real content-based video search. However, due to the complexity of both video data and semantic concepts, existing techniques on automatic video annotation are still not able to handle large-scale video set and large-scale concept set, in terms of both annotation accuracy and computation cost. To address this problem, in this paper, we propose a scalable framework for annotation-based video search, as well as a novel approach to enable large-scale semantic concept annotation, that is, online multi-label active learning. This framework is scalable to both the video sample dimension and concept label dimension. Large-scale unlabeled video samples are assumed to arrive consecutively in batches with an initial pre-labeled training set, based on which a preliminary multi-label classifier is built. For each arrived batch, a multi-label active learning engine is applied, which automatically selects and manually annotates a set of unlabeled sample-label pairs. And then an online learner updates the original classifier by taking the newly labeled sample-label pairs into consideration. This process repeats until all data are arrived. During the process, new labels, even without any pre-labeled training samples, can be incorporated into the process anytime. Experiments on TRECVID dataset demonstrate the effectiveness and efficiency of the proposed framework.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Existing video search engines have not taken the advantages of video content analysis and semantic understanding. Video search in academia uses semantic annotation to approach content-based indexing. We argue this is a promising direction to enable real content-based video search. However, due to the complexity of both video data and semantic concepts, existing techniques on [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"MSR-TR-2008-103","msr_organization":"","msr_pages_string":"10","msr_page_range_start":"10","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"","msr_other_contributors":"","msr_speaker":"","msr_award":"","msr_affiliation":"","msr_institution":"Microsoft 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