I'd like to create an image classifier that takes a set of labeled images and creates a classifier to be used on some unknown images. Most of the examples I see assume all images are the same size (m x n pixels), but my training set will have pictures of various sizes. This is a problem because training my classifier will have the following steps:
- Load each image into m x n array
- Employ some filter
- Transform m x n array into 1 x (m * n) array
- Feature selection
- Train classifier
The different sized images is a problem because step 3 will yield arrays of different lengths if the images don't have the same number of pixels.
I have a background in statistics, not image processing, and I haven't been able to find any general guidelines for how to handle this problem. I'll be working in Python, and it would also be nice to know if there are libraries perform image resizing.