Semi-supervised Cast Indexing for Feature-Length Films

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.