Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/3220

Semi-supervised cast indexing for feature-length films

Authors Fan, Wei
Wang, Tao
Bouguet, JeanYves
Hu, Wei
Zhang, Yimin
Yeung, Dit-Yan
Issue Date 2007
Source Lecture notes in computer science, v. 4351, 2007, p. 625-635
Summary Cast indexing is a very important application for content-based video browsing and retrieval, since the characters in feature-length films and TV series are always the major focus of interest to the audience. By cast indexing, we can discover the main cast list from long videos and further retrieve the characters of interest and their relevant shots for efficient browsing. This paper proposes a novel cast indexing approach based on hierarchical clustering, semi-supervised learning and linear discriminant analysis of the facial images appearing in the video sequence. The method first extracts local SIFT features from detected frontal faces of each shot, and then utilizes hierarchical clustering and Relevant Component Analysis (RCA) to discover main cast. Furthermore, according to the user's feedback, we project all the face images to a set of the most discriminant axes learned by Linear Discriminant Analysis (LDA) to facilitate the retrieval of relevant shots of specified person. Extensive experimental results on movie and TV series demonstrate that the proposed approach can efficiently discover the main characters in such videos and retrieve their associated shots.
Subjects
ISSN 0302-9743
ISBN 978-3-540-69421-2
Language English
Format Conference paper
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