Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-69423-6_61 | MMM |
Keywords | Field | DocType |
semi-supervised cast indexing,main character,tv series,main cast,feature-length film,relevant component analysis,main cast list,novel cast indexing approach,content-based video browsing,linear discriminant analysis,relevant shot,cast indexing,indexation,semi supervised learning,hierarchical clustering | Hierarchical clustering,Video browsing,Scale-invariant feature transform,Computer vision,Pattern recognition,Computer science,Image processing,Search engine indexing,Image retrieval,Supervised learning,Artificial intelligence,Linear discriminant analysis | Conference |
Volume | ISSN | ISBN |
4351 | 0302-9743 | 3-540-69421-8 |
Citations | PageRank | References |
2 | 0.40 | 9 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Fan | 1 | 4205 | 253.58 |
| Tao Wang | 2 | 238 | 23.70 |
| Jean-Yves Bouguet | 3 | 574 | 42.49 |
| Wei Hu | 4 | 182 | 14.17 |
| Yimin Zhang | 5 | 1536 | 130.17 |
| Dit-Yan Yeung | 6 | 5302 | 277.04 |