On what factors does "Bag of Words" training efficiency depends?

edit

I have to say that ORB features are not found on the border of the image, which, in my case, was really big 1/2, or 1/3 from the area of the image. Is this OK? How can I change this?

Linked to the second point: If I have about 400 images per class, shall I use 40000 clusters? or 40000*number_of_classes? Isn't this too large? How long will it take?

For your second concern: The goal here is to maximize the potential of a query image to be classified very well. Classification is done good when a sweet spot of number of clusters are formed which SVM classifier (or whatever else you're using) is able to use distinctly classify images belonging to different classes. Thus, its good to have a good number of training images, (I'd say go for 1000+ per class), and have clusters = no. of classes * 100.

This could be wrong, or work well. I've not done classification, so I really can' tell, but if you think logically, 1000 clusters having the common descriptors from images of 10 classes will be a really good train data compared to 100 clusters (too much overlapping) for the same, or 1,000,000 (1000 images for 10 classes, continued...)

And from each image, if we save only 200 descriptors, then we will have 200 * 1000 * 10 = 2,000,000 descriptors clustered into 1 million clusters. Which is a lot of spread out area of clusters for an SVM to draw classification distinctiveness between.)