I'm having trouble classifying images using a random forest.
The images all have a very similar scale, but they may be rotated arbitrarily around a fixed point in the image.
The core problem is that the images of the test set vary somewhat from the training images. But in this case, it is crucial to train based on the given training set and to test it on the given test set(!).
It's thus easily concluded that, I "simply" need a very robust random forest.
The classes are:
- class 0: images that show an certain plane part (the inside of the engine)
- class 1: images that don't show that part
My learners (node functions) are simple intensity comparisons, that check if a pixel value is lower than another pixel value in the same image:
- I(u1,v1) < I(u2,v2) -> true/false
My current configuration is:
- supervised learning, all class lables available
- number of positive samples: 100 000
- number of negative samples: 100 000
- image size: 160x120
- tree depth = 6
- number of trees: 400
Do you have any suggestions for me?