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Applying Computer
Vision to Art History
John Resig - http://ejohn.org/research/
Visiting Researcher, Ritsumeikan University
What “Works” Today
Reading license plates, zip codes, checks
Optical Character
Recognition
• Tesseract
• https://

code.google.com/
p/tesseract-ocr/
What “Works” Today
Face recognition
Face Matching

• OpenBR
• http://openbiometrics.org/
What “Works” Today
Recognition of flat, textured, objects
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
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.
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”
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.
imgSeek
• Compares entire
image.

• Finds similar images,
not exact.

• Does not find parts of
an image.

• Color sensitive.
• Compares

portions of
images.

• Finds exact
matches.

• Finds images
inside other
images.

• Color
Ukiyo-e.org (Using MatchEngine)

insensitive.
Applying Computer Vision to Art History
Applying Computer Vision to Art History
Applying Computer Vision to Art History
Applying Computer Vision to Art History
Anonymous Italian Art (Frick PhotoArchive)
Using MatchEngine
Conservation
Copies
Image Portion

Partial Image vs. Much Larger Image
Image Categorization
• Deep neural networks
• Requires minimal categorization
• Very little user-input required.
• Ersatz
• http://ersatz1.com/
Requires a lot of training
data (thousands of images)
Takes a lot of computers
(Not cheap)
The less categories you
have, the better.
Applying Computer Vision to Art History
Applying Computer Vision to Art History
Applying Computer Vision to Art History
General Computer
Vision
• Ideal for some supervised training problems
• CCV
• http://libccv.org/
• https://github.com/liuliu/ccv
• OpenCV
• http://opencv.org/
Object Detection
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
Learn More about
Computer Vision
• Learn more:
• http://cs.brown.edu/courses/csci1430/
• Just published paper on Frick Computer
Vision work:

• http://ejohn.org/research/

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