International Journal of Computer Vision
Automatic partitioning of full-motion video
Multimedia Systems
Segmentation of video by clustering and graph analysis
Computer Vision and Image Understanding
Visual information retrieval
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spectral structuring of home videos
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Computable scenes and structures in films
IEEE Transactions on Multimedia
Detection and representation of scenes in videos
IEEE Transactions on Multimedia
Video scene segmentation using Markov chain Monte Carlo
IEEE Transactions on Multimedia
Automated high-level movie segmentation for advanced video-retrieval systems
IEEE Transactions on Circuits and Systems for Video Technology
Efficient video indexing scheme for content-based retrieval
IEEE Transactions on Circuits and Systems for Video Technology
A heuristic algorithm for video scene detection using shot cluster sequence analysis
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
Dominant sets based movie scene detection
Signal Processing
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Text extraction from videos using a hybrid approach
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
A design-of-experiment based statistical technique for detection of key-frames
Multimedia Tools and Applications
A general Framework of video segmentation to logical unit based on conditional random fields
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
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Video indexing requires the efficient segmentation of video into scenes. The video is first segmented into shots and a set of key-frames is extracted for each shot. Typical scene detection algorithms incorporate time distance in a shot similarity metric. In the method we propose, to overcome the difficulty of having prior knowledge of the scene duration, the shots are clustered into groups based only on their visual similarity and a label is assigned to each shot according to the group that it belongs to. Then, a sequence alignment algorithm is applied to detect when the pattern of shot labels changes, providing the final scene segmentation result. In this way shot similarity is computed based only on visual features, while ordering of shots is taken into account during sequence alignment. To cluster the shots into groups we propose an improved spectral clustering method that both estimates the number of clusters and employs the fast global k-means algorithm in the clustering stage after the eigenvector computation of the similarity matrix. The same spectral clustering method is applied to extract the key-frames of each shot and numerical experiments indicate that the content of each shot is efficiently summarized using the method we propose herein. Experiments on TV-series and movies also indicate that the proposed scene detection method accurately detects most of the scene boundaries while preserving a good tradeoff between recall and precision.