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I think it is very interesting to see the keypoint matching problem formulated as a classification problem, in order to reduce the run time complexity. The synthesize view sets is a good idea to fight distortions and blur, but I guess they depend on the object to detect, since some objects may fire just few keypoints (or way to many). Adding white noise should help for recognize keypoints in cluttered backgrounds? This is still not very clear to me.
Using randomized trees it would indeed make fast matching, since we store a bunch of patch examples, but it is concerning that we have to deal with the issue of storing them on memory (It is still a lot just for one object). Building more than one tree seems to be a good approach for searching, but it is still not optimal (same size still).
The comparison made with SIFT keypoints may not be fair, since the software used could be the demo and not the licenced (or real) SIFT version. Moreover, other descriptors should have been good to include in the comparison.
This approach should work very efficiently for tracking objects, since the tree implementation uses simple and efficient tests.

hmm... I see.. The keypoint is one pixel, and for each keypoint, they choose a neighborhood of pixels around it. All these neighborhoods of keypoints from all the images help generate the view set. I wonder how they control for the size of neighborhood? Perhaps that does depends on the image resolution. (Perhaps [19] talks more about this.)

Just to clarify, keypoints are single pixels, right? (not clusters of pixels?) If so, I am curious how robust the authors' method is when varying the resolution of the images.

How many degrees of freedom in thier model for deformation? Dunno if they said, but I must have missed it if they did. I'm guessing it has to be a low number if they're running a ransac or ransac based algorithm for the matching.

I thought the way they synthesized a model to generate a large training set was interesting. More specifically, the way they added noise to an image patch and composited the image patch over a noisy background to improve realism and thus improve keypoint classification under large positional shifts with cluttered backgrounds (page 9). Do other methods do this (adding white noise to improve performance), or is this unique to the authors' method?
Also the authors compared their method to SIFT. It would be interesting to see a comparison to SURF or other improved methods.

The authors mention that they use their own "simple and fast" keypoint detector and that it suffices for operation under even large perspective and scale variations. It would be worthwhile to plug in other simple keypoint detectors to see if a potentially more robust one exists. Also, while it would be more computationally intensive, it would be interesting to see if and how much using a more sophisticated keypoint detector improves the detection and tracking performance of their system.

I think it is very interesting that the training set is potentially infinite since they synthesize "view sets".

By request, here is a conference paper by Lepetit et al. that lead to the journal article that's linked on the 252c site: http://cvlab.epfl.ch/publications/publicat...005/LepetitLF05.pdf

I really like how the paper proposes a different approach for wide-baseline matching; viewing it as a classification problem.
I'm not that familiar with classification methods, but the author mentions PCA and Randomized Trees along with kernel PCA as classification techniques, however, he doesn't provide any performance comparisons between Randomized Trees and PCA or any other classification technique such as k-nearest neighbor, svm, or neural networks.
Is Randomized Tree's advantage over these other techniques obvious?
And, are there any other classification techniques currently being used for wide-baseline matching?