A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A fuzzy video content representation for video summarization and content-based retrieval
Signal Processing - Special issue on fuzzy logic in signal processing
Applications of Video-Content Analysis and Retrieval
IEEE MultiMedia
Video Browsing and Retrieval Based on Multimodal Integration
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Video abstraction: A systematic review and classification
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Video Browsing Using Interactive Navigation Summaries
CBMI '09 Proceedings of the 2009 Seventh International Workshop on Content-Based Multimedia Indexing
Global contrast based salient region detection
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
A unified model for techniques on video-shot transition detection
IEEE Transactions on Multimedia
IEEE Transactions on Multimedia
A generic framework of user attention model and its application in video summarization
IEEE Transactions on Multimedia
Multi-View Video Summarization
IEEE Transactions on Multimedia
Non-sequential video content representation using temporal variation of feature vectors
IEEE Transactions on Consumer Electronics
Semantic-Based Surveillance Video Retrieval
IEEE Transactions on Image Processing
Object-based video abstraction for video surveillance systems
IEEE Transactions on Circuits and Systems for Video Technology
Optimal content-based video decomposition for interactive video navigation
IEEE Transactions on Circuits and Systems for Video Technology
MINMAX optimal video summarization
IEEE Transactions on Circuits and Systems for Video Technology
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With the fast evolution of digital video, research and development of new technologies are greatly needed to lower the cost of video archiving, cataloging and indexing, as well as improve the efficiency and accessibility of stored video sequences. A number of methods to respectively meet these requirements have been researched and proposed. As one of the most important research topics, video abstraction helps to enable us to quickly browse a large video database and to achieve efficient content access and representation. In this paper, a video abstraction algorithm based on the visual attention model and online clustering is proposed. First, shot boundaries are detected and key frames in each shot are extracted so that consecutive key frames in a shot have the same distance. Second, the spatial saliency map indicating the saliency value of each region of the image is generated from each key frame and regions of interest (ROI) is extracted according to the saliency map. Third, key frames, as well as their corresponding saliency map, are passed to a specific filter, and several thresholds are used so that the key frames containing less information are discarded. Finally, key frames are clustered using an online clustering method based on the features in ROIs. Experimental results demonstrate the performance and effectiveness of the proposed video abstraction algorithm.