Projection detecting filter for video cut detection
MULTIMEDIA '93 Proceedings of the first ACM international conference on Multimedia
Automatic partitioning of full-motion video
Multimedia Systems
A feature-based algorithm for detecting and classifying scene breaks
Proceedings of the third ACM international conference on Multimedia
Segmentation of video by clustering and graph analysis
Computer Vision and Image Understanding
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance Characterization and Comparison of Video Indexing Algorithms
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM SIGGRAPH 2005 Papers
Photo tourism: exploring photo collections in 3D
ACM SIGGRAPH 2006 Papers
Modeling, Clustering, and Segmenting Video with Mixtures of Dynamic Textures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extracting Motion Annotations from MPEG-2 Compressed Video for HDTV Content Management Applications
ICMCS '99 Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2
Beyond pixels: exploring new representations and applications for motion analysis
Beyond pixels: exploring new representations and applications for motion analysis
Direct Computation of the Focus of Expansion
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Multimedia
Automated high-level movie segmentation for advanced video-retrieval systems
IEEE Transactions on Circuits and Systems for Video Technology
Rapid estimation of camera motion from compressed video with application to video annotation
IEEE Transactions on Circuits and Systems for Video Technology
A Formal Study of Shot Boundary Detection
IEEE Transactions on Circuits and Systems for Video Technology
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In this paper, we present a novel learning-based algorithm for temporal segmentation of a video into clips based on both camera and scene motion, in particular, based on combinations of static vs. dynamic camera and static vs. dynamic scene. Given a video, we first perform shot boundary detection to segment the video to shots. We enforce temporal continuity by constructing a Markov Random Field (MRF) over the frames of each video shot with edges between consecutive frames and cast the segmentation problem as a frame level discrete labeling problem. Using manually labeled data we learn classifiers exploiting cues from optical flow to provide evidence for the different labels, and infer the best labeling over the frames. We show the effectiveness of the approach using user videos and full-length movies. Using sixty full-length movies spanning 50 years, we show that the proposed algorithm of grouping frames purely based on motion cues can aid computational applications such as recovering depth from a video and also reveal interesting trends in movies, which finds itself interesting novel applications in video analysis (time-stamping archive movies) and film studies.