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Applying Computer Vision to Art History

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Applying Computer Vision to Art History

  1. 1. Applying Computer Vision to Art History John Resig - http://ejohn.org/research/ Visiting Researcher, Ritsumeikan University
  2. 2. What “Works” Today Reading license plates, zip codes, checks
  3. 3. Optical Character Recognition • Tesseract • https:// code.google.com/ p/tesseract-ocr/
  4. 4. What “Works” Today Face recognition
  5. 5. Face Matching • OpenBR • http://openbiometrics.org/
  6. 6. What “Works” Today Recognition of flat, textured, objects
  7. 7. ComputerVision • 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. 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. 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. 10. Image Similarity • imgSeek (Open Source) • http://www.imgseek.net/ • TinEye’s MatchEngine • http://services.tineye.com/MatchEngine • Both are completely unsupervised. No training data is required.
  11. 11. imgSeek • Compares entire image. • Finds similar images, not exact. • Does not find parts of an image. • Color sensitive.
  12. 12. Ukiyo-e.org (Using MatchEngine) • Compares portions of images. • Finds exact matches. • Finds images inside other images. • Color insensitive.
  13. 13. Anonymous Italian Art (Frick PhotoArchive)
 Using MatchEngine
  14. 14. Conservation
  15. 15. Copies
  16. 16. Partial Image vs. Much Larger Image Image Portion
  17. 17. Frick 420 420 Zeri 1583642090 Frick 417 417 ?
  18. 18. 8132a 8132 57129 57134 57130 57138 8131a 8131 ?
  19. 19. Image Categorization • Deep neural networks • Requires minimal categorization • Very little user-input required. • Ersatz • http://www.ersatzlabs.com/
  20. 20. Requires a lot of training data (thousands of images) Takes a lot of computers (Not cheap) The less categories you have, the better.
  21. 21. General Computer Vision • Ideal for some supervised training problems • CCV • http://libccv.org/ • https://github.com/liuliu/ccv • OpenCV • http://opencv.org/
  22. 22. Object Detection
  23. 23. 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
  24. 24. Learn More about ComputerVision • Learn more: • http://cs.brown.edu/courses/csci1430/ • Paper on Frick ComputerVision work: • http://ejohn.org/research/

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