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Appl."],"published-print":{"date-parts":[[2014,2]]},"abstract":"<jats:p>We often remember images and videos that we have seen or recorded before but cannot quite recall the exact venues or details of the contents. We typically have vague memories of the contents, which can often be expressed as a textual description and\/or rough visual descriptions of the scenes. Using these vague memories, we then want to search for the corresponding videos of interest. We call this \u201cMemory Recall based Video Search\u201d (MRVS). To tackle this problem, we propose a video search system that permits a user to input his\/her vague and incomplete query as a combination of text query, a sequence of visual queries, and\/or concept queries. Here, a visual query is often in the form of a visual sketch depicting the outline of scenes within the desired video, while each corresponding concept query depicts a list of visual concepts that appears in that scene. As the query specified by users is generally approximate or incomplete, we need to develop techniques to handle this inexact and incomplete specification by also leveraging on user feedback to refine the specification. We utilize several innovative approaches to enhance the automatic search. First, we employ a visual query suggestion model to automatically suggest potential visual features to users as better queries. Second, we utilize a color similarity matrix to help compensate for inexact color specification in visual queries. Third, we leverage on the ordering of visual queries and\/or concept queries to rerank the results by using a greedy algorithm. Moreover, as the query is inexact and there is likely to be only one or few possible answers, we incorporate an interactive feedback loop to permit the users to label related samples which are visually similar or semantically close to the relevant sample. Based on the labeled samples, we then propose optimization algorithms to update visual queries and concept weights to refine the search results. We conduct experiments on two large-scale video datasets: TRECVID 2010 and YouTube. The experimental results demonstrate that our proposed system is effective for MRVS tasks.<\/jats:p>","DOI":"10.1145\/2534409","type":"journal-article","created":{"date-parts":[[2014,2,11]],"date-time":"2014-02-11T14:39:42Z","timestamp":1392129582000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Memory recall based video search"],"prefix":"10.1145","volume":"10","author":[{"given":"Jin","family":"Yuan","sequence":"first","affiliation":[{"name":"National University of Singapore, Singapore"}]},{"given":"Yi-Liang","family":"Zhao","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore"}]},{"given":"Huanbo","family":"Luan","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore"}]},{"given":"Meng","family":"Wang","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore"}]},{"given":"Tat-Seng","family":"Chua","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore"}]}],"member":"320","published-online":{"date-parts":[[2014,2,14]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proceedings of the TRECVID Workshop.","author":"Amir A.","year":"2005","unstructured":"A. Amir , J. Argillandery , 2005 . IBM Research TRECVID-2005 video retrieval system . In Proceedings of the TRECVID Workshop. A. Amir, J. Argillandery, et al. 2005. IBM Research TRECVID-2005 video retrieval system. In Proceedings of the TRECVID Workshop."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1015330.1015424"},{"key":"e_1_2_1_3_1","first-page":"1208","article-title":"Video retrieval using dialogue, keyframe similarity and video objects","volume":"3","author":"Browne P.","year":"2005","unstructured":"P. Browne and A. F. Smeaton . 2005 . Video retrieval using dialogue, keyframe similarity and video objects . In Proceedings of the International Conference on Image Process 3 , 1208 -- 1211 . P. Browne and A. F. Smeaton. 2005. Video retrieval using dialogue, keyframe similarity and video objects. In Proceedings of the International Conference on Image Process 3, 1208--1211.","journal-title":"Proceedings of the International Conference on Image Process"},{"key":"e_1_2_1_4_1","volume-title":"TRECVID 2010 Known-item Search (KIS) task by I2R. In Proceedings of the TRECVID Workshop.","author":"Chaisorn L.","year":"2010","unstructured":"L. Chaisorn , K. W. Wan , 2010 . TRECVID 2010 Known-item Search (KIS) task by I2R. In Proceedings of the TRECVID Workshop. L. Chaisorn, K. W. Wan, et al. 2010. TRECVID 2010 Known-item Search (KIS) task by I2R. In Proceedings of the TRECVID Workshop."},{"volume-title":"Proceedings of the TRECVID Workshop.","author":"Chang S.-F.","key":"e_1_2_1_5_1","unstructured":"S.-F. Chang , W. H. Hsu , W. Jiang , L. S. Kennedy , D. Xu , A. Yanagawa , and E. Zavesky . 2006. Columbia University Trecvid-2006 video search and high-level feature extraction . In Proceedings of the TRECVID Workshop. S.-F. Chang, W. H. Hsu, W. Jiang, L. S. Kennedy, D. Xu, A. Yanagawa, and E. Zavesky. 2006. Columbia University Trecvid-2006 video search and high-level feature extraction. In Proceedings of the TRECVID Workshop."},{"key":"e_1_2_1_6_1","volume-title":"TRECVID 2010 known-item search by NUS. In Proceedings of the TRECVID Workshop.","author":"Chen X. Y.","year":"2010","unstructured":"X. Y. Chen , J. Yuan , 2010 . TRECVID 2010 known-item search by NUS. In Proceedings of the TRECVID Workshop. X. Y. Chen, J. Yuan, et al. 2010. TRECVID 2010 known-item search by NUS. In Proceedings of the TRECVID Workshop."},{"key":"e_1_2_1_7_1","volume-title":"Color Appearance Models","author":"Fairchild M. D.","unstructured":"M. D. Fairchild . 2005. Color Appearance Models 2 nd Ed. Addison-Wesley . M. D. Fairchild. 2005. Color Appearance Models 2nd Ed. Addison-Wesley.","edition":"2"},{"key":"e_1_2_1_8_1","doi-asserted-by":"crossref","unstructured":"M. R. Hestenes. 1969. Multiplier and gradient methods. J. Optimization Theory Appl. 303--320.  M. R. Hestenes. 1969. 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