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Applying Computer Vision to Art History


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From the February 11th 2014 THATCamp CAA session on applying computer vision techniques to art history research, facilitated by John Resig.

From the February 11th 2014 THATCamp CAA session on applying computer vision techniques to art history research, facilitated by John Resig.

Published in: Technology

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  • 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: •