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Ok let me answer some problems here
1) Long training time is normal. At the last stage, all image windows need to be parsed through the algorithm to check if the final solution is the best one. 52 hours is nothing compared to the fact that a 18 stage HAAR classifier could easily train multiple days. If you want training to be faster, use the LBP feature type, which is an integer type feature and which calculates A LOT faster.
2) Do not supply training samples with white background. You create abnormal edge characteristics, which will lead to features that are good for your set, but never in a real life example. Your image tests show that I am right. If you keep the background, it will be filtered out, because it varies alot over images. The 'general' dog appearance however, will be continuesly and will get good features.
So basically, retraining with the original images (not whitened out) will give you a way better result.
