What does BOWTrainer::clear() exactly do?

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And if I have many images like 1000 per class? Isn't it better to use just the best 500? I choose randomly the images and every time I get a different error rate, So, I pick just the best classifier. Thanks the advice.

In general: the more images you have the better for the classifier (e.g. SVM) training of the BoW-descriptors. What do you mean with you just use the 500 best? And what do you mean with randomly choosing images and error rate? You have one BoWTrainer which trains you the vocabulary which you need to compute the BoW-Descriptors (one for each image), finally you classify them.

The more photos, the best train is false. That is why the term overfitting was invented. Am I wrong: in to many images you will find a lot of variations?

You can overfit your training model if you don't test it on a seperate test set. So yes, you can of course overfit, if your classifier doesn't generalize well on unseen data, but I wouldn't worry for it if you have separate train/validation/test sets.

I do not have. And for each train picking randomly the images (the remaining ones I use for test) I get a different error rate

Okay, this would be one option, but then you need many runs and average the results, however I'd rather do either cross-validation or select and define a train/(validation/)test set, e.g. select randomly 60% of each class for training, 20% for validation (the set which you use to calibrate the best classifier parameters) and a test set for the final results.

Yes, I was using a number (fixed) of images for training and the rest I use for testing