Semantic image segmentation using machine learning (DRAFT)

 

 

 

Decision tree based image segmentation

Activity: Semantic image segmentation

• Open image: xy_8bit__em_fly_eye.tif
• Segment three classes: background, eye, other
  • Choose image filters
  • Draw few labels in the blurry image background => class00
  • Draw few labels on the eye => class01
  • Draw few labels on other parts of the animal => class02
  • While( not happy):
    • Train the classifier - Inspect the predictions
    • Add more labels where the predictions are wrong

TODO: use multiple files to demo that a classifier can be applied on other images.

Formative assessment

True or false? Discuss with your neighbour!

• In contrast to simple thresholding, using machine learning for pixel classification, one always has more than 2 classes.
• If one wants to learn 4 different classes one has to, at least, add 4 annotations on the training image.
• One cannot classify an image where one did not put any training annotations.