title: Advantages of SIFT over HOG
body: <p>I am studying SIFT (Scale-invariant feature transform)
   and HOG (Histogram of Oriented Gradient). So I want to know the advantages of SIFT over HOG in case when we have to extract features from a sequence of frames of an activity (i.e. standing).
  Until now what i have understood:<br>
  1) In SIFT gaussian smoothing is applied in order to compute the DOG (difference of gaussian). Then performing Scale Extrema Detection you will detect the feature points. Once you have this feature points you will need to compute the HOG for each feature. Since it  takes a 16x16 neighbourhood the result will be a 128 length descriptor. Whereas HOG compute edge gradient of a whole image and  find orientation of each pixel so it can generate a histogram.<br>
  2) HOG is used to extract global feature whereas SIFT is used for extracting local features.<br>
  3) SIFT is also scale and rotation invariant whereas HOG is not scale and rotation invariant.</p>
  
  <p>Please let me know if there is any other advantages of SIFT. Also correct me if i have wrong understanding.
  Thank you</p>