1.
Applying Computer
Vision to Art History
John Resig - http://ejohn.org/research/
Visiting Researcher, Ritsumeikan University

2.
What “Works” Today
Reading license plates, zip codes, checks

3.
Optical Character
Recognition
• Tesseract
• https://
code.google.com/
p/tesseract-ocr/

4.
What “Works” Today
Face recognition

5.
Face Matching
• OpenBR
• http://openbiometrics.org/

6.
What “Works” Today
Recognition of flat, textured, objects

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.
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)
• http://www.imgseek.net/
• TinEye’s MatchEngine
• http://services.tineye.com/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.
Ukiyo-e.org (Using MatchEngine)
• Compares
portions of
images.
• Finds exact
matches.
• Finds images
inside other
images.
• Color
insensitive.

13.
Anonymous Italian Art (Frick PhotoArchive)
Using MatchEngine

14.
Conservation

15.
Copies

16.
Partial Image vs. Much Larger Image
Image Portion

17.
Frick 420
420
Zeri 1583642090
Frick 417
417
?

18.
8132a 8132
57129
57134
57130
57138
8131a 8131
?

19.
Image Categorization
• Deep neural networks
• Requires minimal categorization
• Very little user-input required.
• Ersatz
• http://www.ersatzlabs.com/

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.
General Computer
Vision
• Ideal for some supervised training problems
• CCV
• http://libccv.org/
• https://github.com/liuliu/ccv
• OpenCV
• http://opencv.org/

22.
Object Detection

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.
Learn More about
ComputerVision
• Learn more:
• http://cs.brown.edu/courses/csci1430/
• Paper on Frick ComputerVision work:
• http://ejohn.org/research/