# 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

If you are focusing just on pre-processing, you should try different techniques of image augmentation, which include standardization, rotation, flipping, shifts and shears etc. Here are 2 good links: