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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
by jon6 15y ago
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).
- sakai 15y agoI 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."