  We present an active learning strategy for training parametric models of
distance metrics, given triplet-based similarity assessments: object $x_i$ is
more similar to object $x_j$ than to $x_k$. In contrast to prior work on
class-based learning, where the fundamental goal is classification and any
implicit or explicit metric is binary, we focus on {\em perceptual} metrics
that express the {\em degree} of (dis)similarity between objects. We find that
standard active learning approaches degrade when annotations are requested for
{\em batches} of triplets at a time: our studies suggest that correlation among
triplets is responsible. In this work, we propose a novel method to {\em
decorrelate} batches of triplets, that jointly balances informativeness and
diversity while decoupling the choice of heuristic for each criterion.
Experiments indicate our method is general, adaptable, and outperforms the
state-of-the-art.
