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Automatic Photo Annotator

Automatic Photo Annotator. Bryan Klimt May 10, 2005. Photo Annotator. Photos must be labeled with keywords to enable effective search Hand labeling photos is a tedious process Automatic Photo Annotator labels incoming photos based on statistical learning

brooke-forbes
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Automatic Photo Annotator

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  1. Automatic Photo Annotator Bryan Klimt May 10, 2005

  2. Photo Annotator • Photos must be labeled with keywords to enable effective search • Hand labeling photos is a tedious process • Automatic Photo Annotator labels incoming photos based on statistical learning • Annotator then learns from user corrections

  3. Most common keywords • Colors or Patterns • sky, water, trees, cloud, hazy • People • bryan, amy, shyam • Locations • china, italy, pisa, tiantan, florence, firenze, beijing, lucca

  4. Types of Classifiers • Color-based methods • color histograms • cumulative color histograms • Face-based methods • Face detection • Face recognition • Time-based methods • k Nearest Neighbors

  5. Color-based Performance

  6. Cumulative Color

  7. Why does “color” fail? • Photos that are mislabeled (because the feature is not the focus of the photo). • Photos with similar color-patterns, but of completely different subjects.

  8. Hazy?

  9. Water?

  10. Face-based Performance

  11. Faces

  12. Faces?

  13. Time-based Results

  14. Final Results

  15. Conclusions • Time-based annotator performed best • Color-based annotator has inherent limits • Face-based annotator may become important with more training data

  16. Future Work • Hybrid Methods • Pattern-based annotators • Improving face recognizer with unlabeled data?

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