Learning Tversky Similarity
This work addresses semantic similarity for image comparison, but it appears incremental as it applies an existing model to new learning contexts.
The paper tackled the problem of learning Tversky similarity measures from training data to compare objects like images in a semantically meaningful way, and it showed that the approach performs very well compared to existing methods on two image datasets.
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.