Clashifier
Clashifier is open source software, and is part of open source research by the Public Laboratory for Open Technology and Science.
To use Clashifier, you can:
- Train it with a "classname" and a set of corresponding color bands (red, green, blue, near-infrared)
- Find the nearest classname to a set of color bands (and their relative cartesian distance)
- (soon) point it at the URL of an image to get back an image color-coded by classname
- (soon) "draw" on an image in-browser with a colored "pen" to classify pixels from that image and train a model
We hope this will be useful for:
- automatically classifying aerial imagery by land use or type
- detecting and quantifying geographic events like oil spills or chemical seeps
- identifying different plant species, especially in a monoculture as found in wetlands
- (maybe?) identifying crop diseases
This project is in early-stage development and we really need all the help we can get! Get in touch on the Public Laboratory mailing list (sign up at publiclaboratory.org/user/register to join) and pitch in!
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Depends on:
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- RMagick
- rmagick gems, paperclip
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To do:
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- start with a few sample images and set up Fred to grab pixel colors based on clicks (naming a class in an input field)
- allow submission of batches of pixels and start collecting sets of pixels based on dragging -- "painting"
- try coloring what you've "painted"
- Set up image uploading with Paperclip
- histogram an image by classname
- generate a new image colored by classname, with an HTML key
- create an image proxy which colors by classname
Later on:
- Consider normalized RGB: R/(R+G+B) to reduce lighting effects?
- Other classification techniques: SVM, KNN, Neural network
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Helpful reading
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"Naive Bayes Classification in Ruby using Hadoop and HBase"
"Bayesian marker extraction for color watershed in segmenting microscopic images"
- Olivier Lezoray, Hubert Cardot
"Classifier gem: Classifier is a general module to allow Bayesian and other types of classifications."
"Progress in pattern recognition, image analysis and applications"
"Ways to improve Image Pixel Classification"