Processing a heterogeneous image-collection for CBIR
From what you said in the comments, it seems that CBIR is not really what you need here.
  I am guessing that you would help the historians more by clustering the images, rather than by doing 
retrieval.  

SIFT and SURF are a good place to start.  You might also try the FREAK descriptor, which you can compute at the 
points detected by SIFT or SURF.

Then the important question is what you do with all those features.  For a problem like this you should consider the bag-of-features approach or the pyramid match kernel.

Edit: you may be right to look for a different interest point detector. SIFT and SURF detectors give you centers of blobs (roughly circular patches of uniform intensity).  Since you are looking at man-made gravestones with Hebrew text on them, corners might work better.  A classic approach is the Harris-Laplacian corner detector.  Note that the original Harris detector is not multi-scale.  For this application you definitely need multiple scales.  Other corner detectors such as FAST and AGAST have been proposed recently.  I have heard good things about AGAST, but I have not tried it.