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Video Analysis via Nonlinear Dimensionality Reduction Techniques

Explore the use of Nonlinear Dimensionality Reduction techniques LLE and Isomap to analyze video trajectories. Input high-dimensional image sequences are transformed into low-dimensional embeddings for better analysis. The technique uses the L2 Norm as a distance metric, providing a powerful tool to discover intrinsic low dimensionality in video trajectories. Future work includes improving distance metrics and expanding applications.

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Video Analysis via Nonlinear Dimensionality Reduction Techniques

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  1. Video Analysis via Nonlinear Dimensionality Reduction Technique Yi Li Colin Zheng

  2. Roadmap • Nonlinear Dimensionality Reduction • LLE • Isomap • Video Analysis • A trajectory thorough an image space • Low dimension embedding

  3. Problem Setting • Input • Image Sequence: each image is a point in a high dimensional space • Analysis • Use nonlinear dimensionality reduction technique • Use L2 Norm as distance metric • Output • A trajectory in a low dimensional space

  4. Bird Flying Sequence Input Tracking

  5. Embedding

  6. Scaling vs. non-scaling

  7. Resized Bird

  8. Temporal Super-Resolution

  9. Ellipse Fitting

  10. Running Original Input

  11. LLE Embedding

  12. Isomap Embedding

  13. Fitting Input Input Original Output

  14. Conclusion • A powerful tool to discover the intrinsic low dimensionality in video trajectory • Future Work • Better distance metric • More applications

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