Though elegant in theory, support vector machines are quite slow if the number of candidate predictors is above 100. In fact, oftentimes there are computational issues even when the number of candidate predictors is 10+. Never had a good experience with SVM.

In your case, every "clever" feature aggregating several/many pixels is a candidate predictor (whether the feature is obtained via wavelet analysis, principal component analysis or somehow differently). Therefore, I would advise you to start with random forests or gradient boosting.

That is the generic approach, of course. An even better procedure would be examining recent research of your specific problem (the type of images that you study). I'm sure shortcuts have been developed and you will proceed accordingly.

Though elegant in theory, support vector machines are quite slow if the number of candidate predictors is above 100. In fact, oftentimes there are computational issues even when the number of candidate predictors is 10+. Never had good experience with SVM.

In your case, every "clever" feature aggregating several/many pixels is a candidate predictor (whether the feature is obtained via wavelet analysis, principal component analysis or somehow differently). Therefore, I would advise you to start with random forests or gradient boosting.

That is the generic approach, of course. An even better procedure would be examining recent research of your specific problem (the type of images that you study). I'm sure shortcuts have been developed and you will proceed accordingly.