What does it mean an histogram vector normalization with L1/L2 norms? 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

What does the bold phrase means?
 A: $L_p$ normalization is actually described in Wikipedia that quotes Dalal
and Triggs (2005)

Dalal and Triggs explored four different methods for block
  normalization. Let $v$ be the non-normalized vector containing all
  histograms in a given block, $\|v\|_k$ be its $k$-norm for $k={1,2}$
  and $e$ be some small constant (the exact value, hopefully, is
  unimportant). Then the normalization factor can be one of the
  following:
L2-norm: $f = {v \over \sqrt{\|v\|^2_2+e^2}}$
(...)
L1-norm: $f = {v \over (\|v\|_1+e)}$
L1-sqrt: $f = \sqrt{v \over (\|v\|_1+e)}$

So $L_p$ normalization of histograms in fact relates to using $L_p$ norms to normalize vectors.
By "dimensions" authors mean the "length" of vectors.

Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) (Vol. 1, pp. 886-893). IEEE.
A: Well, if I had to guess what the slide is referring to:


*

*$L_{2}$ normalization
$$x_i'=\frac{x_i}{\|x\|_2}=\frac{x_i}{\left(\sum_j{x_j^2}\right)^{1/2}}$$

*$L_{1}$ normalization
$$x_i'=\frac{x_i}{\|x\|_1}=\frac{x_i}{\sum_j{|x_j|}}$$

The $L^{p}$ norm is given by $\|x\|_{p}=\sqrt[p]{\sum_i|x_i|^{p}}, p \in \mathbb{R}$ (link).
