I was reading these slides about Bag of Features (BoF). At slide 23 you can read:
each image is represented by a vector, typically 1000-4000 dimension, normalization with L1/L2 norm
Why we should normalize the feature histogram vector for image-classification/retrieval applications?
In addition, the distance used for normalize histograms should be the same for computing the distance between them? Because the standard distance used for BoF histograms is $\chi2$ distance, not L1/L2.