Cast indexing for videos by NCuts and page ranking

Cast indexing is an important video mining technique which provides audience the capability to efficiently retrieve interested scenes, events, and stories from a long video. This paper proposes a novel cast indexing approach based on Normalized Graph Cuts (NCuts) and Page Ranking. The system first adopts face tracker to group face images in each shot into face sets, and then extract local SIFT feature as the feature representation. There are two key problems for cast indexing. One is to find an optimal partition to cluster face sets into main cast. The other is how to exploit the latent relationships among characters to provide a more accurate cast ranking. For the first problem, we model each face set as a graph node, and adopt Normalized Graph Cuts (NCuts) to realize an optimal graph partition. A novel local neighborhood distance is proposed to measure the distance between face sets for NCuts, which is robust to outliers. For the second problem, we build a relation graph for characters by their co-occurrence information, and then adopt the PageRank algorithm to estimate the Important Factor (IF) of each character. The PageRank IF is fused with the content based retrieval score for final ranking. Extensive experiments are carried out on movies, TV series and home videos. Promising results demonstrate the effectiveness of proposed methods.