Once you created the vocabulary, you have a list of all possible words in the training set(dictionary). Let's do an easy example. The training set contains an 'eye', a 'mouth' and a 'nose'. When a new test image comes, then you are going to extract features and you are going to try to detect these three features. Then, what you do is you create a histogram, having as index these three. 1.eye 2. mouth. 3.nose. For every feature that is similar to an eye, you are going to add a +1. Say that now you test with an image where there are 100 eyes. Then your histogram for that image is going to be [100 0 0] (no mouth and no nose.) Therefore, in this case, this image is represented with 3 components.
100 | 0 | 0
e | m | n
I hope it is clearer now. An image, will kind of have a 'signature' which will be a kind of summary of your features. In this case, the histogram would define perfectly the image as
100 eyes! And it is precisely what the image would be, right?
Let me know if something is not clear!