Think it like this.
You have 10 cluster means total and 6 features for current image. First 3 of those features are closest to 5th mean and remaining 3 of them are closest to 7th, 8th, and 9th mean respectively. Then your feature will be like [0, 0, 0, 0, 3, 0, 1, 1, 1, 0]
or normalized version of this. Which is 10 dimensional, and that is equal to number of cluster mean. So you can create 100000 dimensional vector from 63 features if you want.
But still I think there is something wrong, because after you applied BOW, your features should be 1x100 not 128x100. Your cluster means are 128x1 and you are assigning your 128x1 sized features (you hvae 34 128x1 feature for first image, 63 128x1 feature for second image, etc.) to those means. So in basic you are assigning 34 or 63 features to 100 means, your result should be 1x100.