A user attention model for video summarization
Proceedings of the tenth ACM international conference on Multimedia
Video abstraction: A systematic review and classification
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
A Unified Framework for Video Summarization, Browsing & Retrieval: with Applications to Consumer and Surveillance Video
Hidden Markov Models for Video Skim Generation
WIAMIS '07 Proceedings of the Eight International Workshop on Image Analysis for Multimedia Interactive Services
SmartPlayer: user-centric video fast-forwarding
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
IEEE Transactions on Multimedia - Special issue on integration of context and content
Information theory-based shot cut/fade detection and video summarization
IEEE Transactions on Circuits and Systems for Video Technology
An ICA Mixture Hidden Markov Model for Video Content Analysis
IEEE Transactions on Circuits and Systems for Video Technology
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In this paper, we develop a video player to allow the users to do fast-forward playback based on the semantic video content. The whole system has two modules, processing and playing. In the processing part, we present a video time density function (VTDF) to describe the temporal dynamics of video data first. A VTDF-based temporal quantization method is then developed to find the best quanta and partition in the time domain. The optimal quanta are used to extract key frames. The optimal number of key frames is determined by a temporal mean square error (TMSE)-based criterion. In the playing module, we combine the key frame sequence and a set of parameters together and feed them into a triangle-based transition function to generate the sampled frames in a non-uniform way. A built video player will play all sampled frames in its intelligent fast-forward mode for a given fast-forward speed factor. The implementation of video player demonstrates the feasibility of proposed method in practice.