image matching algorithms
fetchinson at googlemail.com
Tue Mar 11 02:05:12 CET 2008
> > Thanks for the info! SIFT really looks like a heavy weight solution,
> > but do you think the whole concept can be simplified if all I needed
> > was: given a photo, find similar ones? I mean SIFT first detects
> > objects on the image and find similarities, but I don't need the
> > detection part at all, all I care about is similarity for the whole
> > photo. I surely don't understand the big picture fully but just have
> > the general feeling that SIFT and other expert tools are an overkill
> > for me and a simplified version would be just as good with a much more
> > easily comprehensible core algorithm.
> Please describe the kind of photos you are dealing with. Are they
> identical photos, in say different formats or with different metadata?
> Or are they rescaled images? Or maybe they are the same photo cropped
The photos are just coming straight from my digital camera. Same
format (JPEG), varying size (6-10 megapixel) and I would like to be
able to pick one and then query the database for similar ones. For
example: I pick a photo which is more or less a portrait of someone,
the query should return other photos with more or less portraits. If I
pick a landscape with lot of green and a mountain the query should
result in other nature (mostly green) photos. Something along these
lines, of course the matches won't be perfect because I'm looking for
a simple algorithm, but something along these lines.
> SIFT will work in more or less the process you described in your first
> post. It basically calculates the N sets of numbers for each image,
> representing the unique features of that image and their relative
> positions. The rest of the process if up to you. You have to compare the
> different sets of numbers to find the image with the minimal difference,
> as opposed to comparing the whole image.
Great, this sounds very good, I'll give SIFT a try (at least trying to
understand the basic concepts) although at the moment it looks a bit
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