Face annotation using Transductive Kernel Fisher Discriminant
Publication in refereed journal


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摘要Face annotation in images and videos enjoys many potential applications in multimedia information retrieval. Face annotation usually requires many training data labeled by hand in order to build effective classifiers. This is particularly challenging when annotating faces on large-scale collections of media data, in which huge labeling efforts would be very expensive. As a result, traditional supervised face annotation methods often suffer from insufficient training data. To attack this challenge, in this paper, we propose a novel Transductive Kernel Fisher Discriminant (TKFD) scheme for face annotation, which outperforms traditional supervised annotation methods with few training data. I he main idea of our approach is to solve the Fisher's discriminant using deformed kernels incorporating the information of both labeled and unlabeled data. To evaluate the effectiveness of our method, we have conducted extensive experiments on three types of multimedia testbeds: the FRGC benchmark face dataset, the Yahoo! web image collection, and the TRECVID video data collection. The experimental results show that our TKFD algorithm is more effective than traditional supervised approaches, especially when there are very few training data.
著者Zhu JK, Hoi SCH, Lyu MR
期刊名稱IEEE Transactions on Multimedia
出版年份2008
月份1
日期1
卷號10
期次1
出版社IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
頁次86 - 96
國際標準期刊號1520-9210
電子國際標準期刊號1941-0077
語言英式英語
關鍵詞face annotation; image annotation; kernel Fisher discriminant; multimedia information retrieval; supervised learning; transductive kernel Fisher discriminant; transductive learning
Web of Science 學科類別Computer Science; Computer Science, Information Systems; COMPUTER SCIENCE, INFORMATION SYSTEMS; Computer Science, Software Engineering; COMPUTER SCIENCE, SOFTWARE ENGINEERING; Telecommunications; TELECOMMUNICATIONS

上次更新時間 2021-21-02 於 00:46