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Applying Computer Vision to Art History

  1. Applying Computer Vision to Art History John Resig - Visiting Researcher, Ritsumeikan University
  2. What “Works” Today Reading license plates, zip codes, checks
  3. Optical Character Recognition • Tesseract • https:// p/tesseract-ocr/
  4. What “Works” Today Face recognition
  5. Face Matching • OpenBR •
  6. What “Works” Today Recognition of flat, textured, objects
  7. Computer Vision • Unsupervised (requires no labeling): • Comparing an entire image • Categorizing an image • Supervised (requires labeling): • Finding parts of an image • Finding and categorizing parts of an image
  8. Unsupervised Training • Requires little-to-no prepping of data • Can just give the tool a set of images and have it produce results • Extremely easy to get started, results aren’t always as interesting.
  9. Supervised Training • Need lots of training data • Needs to be pre-selected/categorized • Think: Thousands of images. • If your collection is smaller than this, perhaps it may not benefit. • Or you may need crowd sourcing. • Results can be more interesting: • “Find all the people in this image”
  10. Image Similarity • imgSeek (Open Source) • • TinEye’s MatchEngine • • Both are completely unsupervised. No training data is required.
  11. imgSeek • Compares entire image. • Finds similar images, not exact. • Does not find parts of an image. • Color sensitive.
  12. • Compares portions of images. • Finds exact matches. • Finds images inside other images. • Color (Using MatchEngine) insensitive.
  13. Anonymous Italian Art (Frick PhotoArchive) Using MatchEngine
  14. Conservation
  15. Copies
  16. Image Portion Partial Image vs. Much Larger Image
  17. Image Categorization • Deep neural networks • Requires minimal categorization • Very little user-input required. • Ersatz •
  18. Requires a lot of training data (thousands of images) Takes a lot of computers (Not cheap) The less categories you have, the better.
  19. General Computer Vision • Ideal for some supervised training problems • CCV • • • OpenCV •
  20. Object Detection
  21. Training Caveats • Requires thousands (if not 10s of thousands) of images • Will take at least a week to run on a very powerful computer • Does not work with 3D objects
  22. Learn More about Computer Vision • Learn more: • • Just published paper on Frick Computer Vision work: •