  In this paper we propose a non-metric ranking-based representation of
semantic similarity that allows natural aggregation of semantic information
from multiple heterogeneous sources. We apply the ranking-based representation
to zero-shot learning problems, and present deterministic and probabilistic
zero-shot classifiers which can be built from pre-trained classifiers without
retraining. We demonstrate their the advantages on two large real-world image
datasets. In particular, we show that aggregating different sources of semantic
information, including crowd-sourcing, leads to more accurate classification.
