data pre-processing before image classification I'm working on a machine learning project, Images classification (shape: 100 x 100)-> (vector of 10000), I did some pre-processing before applying decision trees algorithm , I got an accuracy of 55 % I tried to change parameters but the accuracy did not increase, Can someone suggest something to do in pre-processing exept what I did :


Denoising Images: Images are extremely noised, so I only kept relevant information in each image.(it works well)
Centring Images: I translated the relevant values for all images to the top left to keep relevent information in same part of my vectors
Resizing Images: I resized each image to (40,40) -> (1600 vector) to make the inputs smaller 


Thank you
Why are you convinced that the issue is in pre-processing? It could be in many other spots or a combination of steps in your process. 

If I were you, I would revisit WHY you're using a decision tree. There are many other approaches to image processing and neural networks are far superior - in most cases - to decision trees.