I want to try to do something like this:
http://cs229.stanford.edu/proj2010/BlessingWen-UsingMachineLearningForIdentificationOfArtPaintings.pdf
But I've noticed that it looks like sort of cutting-edge research. First thing I'd like to know is whether someone who has done a good deal of machine learning before but none on images (me) could realistically hope to accomplish such a thing.
If it is realistic, all I'm looking for is a broad list of steps for what I need to do to get a good result with what OpenCV has on offer. I'm not looking for code, or even pseudocode. I can do all of that once I know what I should do. For example, the paper linked above talks about normalizing the images to 100x100. Do I just take a chunk that size out of the center of the image or something? And the OpenCV books I have on hand don't talk much about how to use the machine learning tools of OpenCV, though they are strangely still documented for some reason. I already know what SVM and decision trees are. All I need to know is how to get the images into a form that is suitable for them. All these features (SIFT, HOG, etc.) are very baffling if you haven't done CV before.