

A better search for visually similar images - pitdesi
http://graphics.cs.cmu.edu/projects/crossDomainMatching/

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modeless
Wow, source code on Github, MIT licensed!
<https://github.com/quantombone/exemplarsvm>

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maxjaderberg
Very nice paper. I am actually working on a project in a similar space using
very similar techniques, though emphasis is on speed of matching and retrieval
as well as matching the objects in the image rather than the image as a whole.
Little early demo here
[http://www.youtube.com/watch?v=h3YldXhG3Qc&feature=chann...](http://www.youtube.com/watch?v=h3YldXhG3Qc&feature=channel_video_title)

Anyway, great work! The paper was a good read.

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vlamingsjef
That's just brilliant. It reminds me a bit of Microsoft's Photosynth.

I think this technique provides a lot of possibilities for future consumers.
Maybe in the future you could cross-match your photos with an online database
and auto-adjust the color and light balance accordingly.

E.g. Let's say you went to visit Machu Picchu on a very cloudy and rainy day.
You come home and realize your photos look terrible. You put your photos in a
piece of software and match them with an online set of Machu Picchu photos.
You click on "auto-adjust" and hey presto, they are transformed in to a set of
photos that look perfectly lit and balanced. Or am I just dreaming out loud.

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sp332
So why bother taking pictures at all, when you can just find better ones
online?

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jon6
Pretty cool. I'm not super familiar with machine learning but from reading
section 2 of their paper it sounds like you need to manually find a matching
picture to the one you care about (the single positive) and a bunch of
pictures that don't match. Then you run the algorithm and come up with a set
of weights.

It would be nice if things were more automatic, like if a computer program
could decide what features were unique (maybe also through machine learning it
could learn that buildings are generally unique and the sky is not).

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sakai
I haven't read the paper in full yet (nor am I a machine learning expert), but
it seems to be training it against a precompiled dataset, not using human
views:

"To learn the feature weight vector which best discriminates an im- age from a
large “background” dataset, we employ the linear Sup- port Vector Machine
(SVM) framework. We set up the learning problem following [Malisiewicz et al.
2011] which has demon- strated that a linear SVM can generalize even with a
single positive example, provided that a very large amount of negative data is
avail- able to “constrain the solution”. However, whereas in [Malisiewicz et
al. 2011] the negatives are guaranteed not to be members of the positive class
(that is why they are called negatives), here this is not the case. The
“negatives” are just a dataset of images randomly sampled from a large Flickr
collection, and there is no guarantee that some of them might not be very
similar to the “positive” query image. Interestingly, in practice, this does
not seem to hurt the SVM, suggesting that this is yet another new application
where the SVM formalism can be successfully applied."

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pi18n
That seems to imply they do need one human-generated positive match, though.

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jacobolus
It would be hard to find matches for an image if you didn’t start with an
image to match, eh?

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pi18n
Haha, yes, but I mean it seems to imply it requires a starting pair; one
additional photo selected by the human as a match in addition to the one we
desire to find matches for.

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abhinavsh
This work only requires one image (The query) and nothing else as positive.

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sakai
Very cool. Figure 7 is particularly instructive as to the power of this
approach.

It would also be interesting to see a comparison of images where the features
are less pronounced, e.g., matching landscape images. One can imagine uniform
weighting approaches performing better where, for example, color is a more
important matching criteria than form/features.

