{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T06:43:46Z","timestamp":1740120226970,"version":"3.37.3"},"reference-count":20,"publisher":"World Scientific Pub Co Pte Ltd","issue":"09","funder":[{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LY16F030010"],"award-info":[{"award-number":["LY16F030010"]}]},{"name":"Lishui Science and Technology foundation","award":["2017RC10"],"award-info":[{"award-number":["2017RC10"]}]},{"DOI":"10.13039\/501100007194","name":"Wenzhou Science and Technology Bureau","doi-asserted-by":"crossref","award":["Y20150086"],"award-info":[{"award-number":["Y20150086"]}],"id":[{"id":"10.13039\/501100007194","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p> Good trimap is essential for high-quality alpha matte. However, making high-quality trimap is hardwork, especially for complex images. In this paper, an active learning framework is proposed to make high quality trimap. There are two active learning methods which are employed: minimization of uncertainty sampling (MUS) and maximization of expected model output change (EMOC). MUS model finds the informative area in image which can decrease the uncertain sampling of alpha matte. EMOC model finds the important areas in image which can give the maximum expected output change of alpha matte. Two methods are combined to define the active map. Active map shows important areas which are informative in image. It can help users to make high quality trimap. The analysis and evaluation of benchmark datasets show that proposed method is effective. <\/jats:p>","DOI":"10.1142\/s0218001419510030","type":"journal-article","created":{"date-parts":[[2018,12,17]],"date-time":"2018-12-17T04:15:52Z","timestamp":1545020152000},"page":"1951003","source":"Crossref","is-referenced-by-count":1,"title":["An Active Learning Framework for Alpha Matting"],"prefix":"10.1142","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7030-0453","authenticated-orcid":false,"given":"Yang","family":"Shen","sequence":"first","affiliation":[{"name":"Computer Science and Technology, Lishui University, Building 18B419, Lishui City 323000, P. R. China"}]},{"given":"Pengjie","family":"Wang","sequence":"additional","affiliation":[{"name":"Computer Science and Technology, Dalian Minzu University, Building 10, Dalian 116000, P. R. China"}]},{"given":"Zhifang","family":"Pan","sequence":"additional","affiliation":[{"name":"School of Information and Engineering, Wenzhou Medical University, Wenzhou 325035, P. R. China"},{"name":"Information Technology Center, Wenzhou Medical University, Wenzhou 325035, P. R. China"}]},{"given":"Yanxia","family":"Bao","sequence":"additional","affiliation":[{"name":"Computer Science and Technology, Lishui University, Building 18B419, Lishui City 323000, P. R. China"}]}],"member":"219","published-online":{"date-parts":[[2019,8,7]]},"reference":[{"key":"S0218001419510030BIB001","first-page":"78.1","volume-title":"Proc. British Machine Vision Conf.","author":"Alireza Fathi X. R.","year":"2011"},{"issue":"1","key":"S0218001419510030BIB002","first-page":"34","volume":"162","author":"Chao Li X. Z. H. l. P.","year":"2017","journal-title":"Comput. Vis. Image Underst."},{"first-page":"869","volume-title":"IEEE Conf. 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