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.
34. 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
35. Learn More about
ComputerVision
• Learn more:
• http://cs.brown.edu/courses/csci1430/
• Paper on Frick ComputerVision work:
• http://ejohn.org/research/