  In this paper, we advocate Tversky's ratio model as an appropriate basis for
computational approaches to semantic similarity, that is, the comparison of
objects such as images in a semantically meaningful way. We consider the
problem of learning Tversky similarity measures from suitable training data
indicating whether two objects tend to be similar or dissimilar.
Experimentally, we evaluate our approach to similarity learning on two image
datasets, showing that is performs very well compared to existing methods.
