title: Image Descriptors with SIFT/VLFEAT
body: <p>I want to perform a classification task in which I map a given image of an object to one of a list of predefined constellations that object can be in (i.e. find the most probable match). 
  In order to get descriptors of the image (on which i will run machine learning algorithms) i was suggested using SIFT with the VLFeat implementation.  </p>
  
  <p>First of all my main question - I would like to ignore the key-point finding part of sift, and only use it for its descriptors. In the tutorial I saw that there is an option to do exactly that by calling </p>
  
  <p><code>
  [f,d] = vl_sift(I,'frames',fc) ;
  </code></p>
  
  <p>where fc specifies the key-points. My problem is that I want to explicitly specify the 
  bounding box in which i want to calculate the descriptors around the key-point - but it seems i can only specify a scale parameter which right now is a bit cryptic to me and doesn't allow me to specify explicitly the bounding box. Is there a way to achieve this?</p>
  
  <p>The second question is does setting the scale manually and getting the descriptors this way make sense? ( i.e. result in a good descriptor? ). Any other suggestions regarding better ways of getting descriptors ? ( using SIFT with other implementations, or other non-SIFT descriptors ). I should mention that my object is always the only object in the image, is centered,  has constant illumination, and changes by some kinds of rotations of its internal parts - And this is why I thought SIFT would work out as i understood it focuses on the orientation gradients which would change accordingly with the rotations of the object. </p>
  
  <p>Thanks</p>