An Eigenspace Projection Clustering Method for Inexact Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Broadcast news story segmentation using social network analysis and hidden markov models
Proceedings of the 15th international conference on Multimedia
Movie/Script: Alignment and Parsing of Video and Text Transcription
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
RoleNet: movie analysis from the perspective of social networks
IEEE Transactions on Multimedia - Special issue on integration of context and content
Character identification in feature-length films using global face-name matching
IEEE Transactions on Multimedia
Detection and representation of scenes in videos
IEEE Transactions on Multimedia
Character-based movie summarization
Proceedings of the international conference on Multimedia
Dominant sets based movie scene detection
Signal Processing
Exploiting content relevance and social relevance for personalized ad recommendation on internet TV
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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Semantic scene segmentation is a crucial step in movie video analysis and extensive research efforts have been devoted to this area. However, previous methods are heavily relying on video content itself, which are lack of objective evaluation criterion and necessary semantic link due to the semantic gap. In this paper, we propose a novel role-based approach for movie scene segmentation using script. Script is a text description of movie content that contains the scene structure information and related character names, which can be regarded as an objective evaluation criterion and useful external reference. The main novelty of our approach is that we convert the movie scene segmentation into a movie-script alignment problem and propose a HMM alignment algorithm to map the script scene structure to the movie content. The promising results obtained from three Hollywood movies demonstrate the effectiveness of our proposed approach.