Semi-supervised Cast Indexing for Feature-Length Films

Abstract. Cast indexing is a very important application for contentbased 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 ...

• Type: Conference
• Year: 2007
• Where: MMM
• Authors: Wei Fan, Tao Wang, Jean-Yves Bouguet, Wei Hu, Yimin Zhang, Dit-Yan Yeung
• Keywords: Cast Indexing, Linear Discriminant Analysis, Main Cast, MMM 2007, Multimedia