Video Snapshot: A Bird View of Video Sequence
MMM '05 Proceedings of the 11th International Multimedia Modelling Conference
Towards optimal bag-of-features for object categorization and semantic video retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Feature fusion and redundancy pruning for rush video summarization
Proceedings of the international workshop on TRECVID video summarization
Automated summarization of narrative video on a semantic level
ICSC '07 Proceedings of the International Conference on Semantic Computing
Video summarisation: A conceptual framework and survey of the state of the art
Journal of Visual Communication and Image Representation
A set theoretical method for video synopsis
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
A Logic Framework for Sports Video Summarization Using Text-Based Semantic Annotation
SMAP '08 Proceedings of the 2008 Third International Workshop on Semantic Media Adaptation and Personalization
Video event detection and summarization using audio, visual and text saliency
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
WordNet::Similarity: measuring the relatedness of concepts
HLT-NAACL--Demonstrations '04 Demonstration Papers at HLT-NAACL 2004
IEEE Transactions on Multimedia - Special issue on integration of context and content
ANSES: summarisation of news video
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
A generic framework of user attention model and its application in video summarization
IEEE Transactions on Multimedia
Automated video program summarization using speech transcripts
IEEE Transactions on Multimedia
IEEE Transactions on Consumer Electronics
Automatic soccer video analysis and summarization
IEEE Transactions on Image Processing
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Video summarization aims to provide a condensed yet informative version for original footages so as to facilitate content comprehension, browsing and delivery, where multi-modal features play an important role in differentiating individual segments of a video. In this paper, we present a method combining both visual and semantic features. Rather than utilize domain specific or heuristic textual features as semantic features, we assign semantic concepts to video segments through automatic video annotation. Therefore, semantic coherence between accompanying text and high-level concepts of video segments is exploited to characterize the importance of video segments. Visual features (e.g. motion and face) which have been widely used in user attention model-based summarization have been integrated with the proposed semantic coherence to obtain the final summarization. Experiments on a halfhour sample video from TRECVID 2006 dataset have been conducted to demonstrate that semantic coherence is very helpful for video summarization when being fused with different visual features.