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Learning the “Epitome” of a Video Sequence Information Processing Workshop 2004

Learning the “Epitome” of a Video Sequence Information Processing Workshop 2004. Vincent Cheung Probabilistic and Statistical Inference Group Electrical & Computer Engineering University of Toronto Toronto, Ontario, Canada Advisor: Dr. Brendan J. Frey. Outline. Image epitome What? Why?

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Learning the “Epitome” of a Video Sequence Information Processing Workshop 2004

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  1. Learning the “Epitome”of a Video SequenceInformation Processing Workshop 2004 Vincent Cheung Probabilistic and Statistical Inference Group Electrical & Computer Engineering University of Toronto Toronto, Ontario, Canada Advisor: Dr. Brendan J. Frey Aug. 11, 2004

  2. Outline • Image epitome • What? • Why? • Implementation computation issues • Efficiently implementing the learning algorithm • Video epitome • Extension to videos • Video inpainting Cheung

  3. Image Epitome • Jojic, N., Frey, B., & Kannan, A. (2003). Epitomic analysis of appearance and shape. In Proc. IEEE ICCV. • Miniature, condensed version of the image • Accurately accounts for the interesting properties of the image • Applications • object detection • texture segmentation • image retrieval • compression Cheung

  4. Image Epitome Examples Cheung

  5. Training Set Input image Epitome Sample Patches Unsupervised Learning e – epitome Tk – mapping Zk – image patch Bayesiannetwork e T1 T2 TM … Z1 Z2 ZM Learning the Image Epitome Cheung

  6. + - + - Shifted Cumulative Sum Algorithm Cheung

  7. Collecting Sufficient Statistics Cheung

  8. Extending Epitomes to Videos • Desire a miniature, condensed version of a video sequence • Want it to accurately account for the interesting properties of the video • Applications • optic flow • segmentation • texture transfer • layer separation • compression • noise reduction • inpainting Cheung

  9. Training Set Input Video Video Epitome Unsupervised Learning Sample Patches Frame 3 Frame 2 Frame 1 Video Epitome Cheung

  10. Temporally Compressed Spatially Compressed Video Epitome Example Cheung

  11. Video Inpainting (1) • Fill in missing portions of a video • damaged films • occluding objects • Reconstruct the missing pixels from the video epitome Cheung

  12. Video Inpainting (2) Cheung

  13. Conclusion • Improved the efficiency of learning image epitomes • Extended the concept of epitomes to video sequences • Demonstrated the ability of video epitomes to model motion patterns through video inpainting Cheung

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