Non-Parametric Motion Activity Analysis for Statistical Retrieval with Partial Query
Journal of Mathematical Imaging and Vision
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MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Computer Vision and Image Understanding - Special isssue on video retrieval and summarization
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Multimedia Tools and Applications
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Proceedings of the 6th ACM international conference on Image and video retrieval
Multimedia Tools and Applications
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IEA/AIE '08 Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence
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International Journal of Artificial Intelligence and Soft Computing
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Multimedia Tools and Applications
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Image Communication
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Journal on Image and Video Processing
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PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
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Journal of the American Society for Information Science and Technology
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IEEE Transactions on Information Theory
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Signal Processing
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Proceedings of the Third International Conference on Internet Multimedia Computing and Service
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IWDW'05 Proceedings of the 4th international conference on Digital Watermarking
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CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
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Content structure plays an important role in the understanding of video. In this paper, we argue that knowledge about structure can be used both as a means to improve the performance of content analysis and to extract features that convey semantic information about the content. We introduce statistical models for two important components of this structure, shot duration and activity, and demonstrate the usefulness of these models with two practical applications. First, we develop a Bayesian formulation for the shot segmentation problem that is shown to extend the standard thresholding model in an adaptive and intuitive way, leading to improved segmentation accuracy. Second, by applying the transformation into the shot duration/activity feature space to a database of movie clips, we also illustrate how the Bayesian model captures semantic properties of the content. We suggest ways in which these properties can be used as a basis for intuitive content-based access to movie libraries