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:

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."