  As we enter into the big data age and an avalanche of images have become
readily available, recognition systems face the need to move from close, lab
settings where the number of classes and training data are fixed, to dynamic
scenarios where the number of categories to be recognized grows continuously
over time, as well as new data providing useful information to update the
system. Recent attempts, like the open world recognition framework, tried to
inject dynamics into the system by detecting new unknown classes and adding
them incrementally, while at the same time continuously updating the models for
the known classes. incrementally adding new classes and detecting instances
from unknown classes, while at the same time continuously updating the models
for the known classes. In this paper we argue that to properly capture the
intrinsic dynamic of open world recognition, it is necessary to add to these
aspects (a) the incremental learning of the underlying metric, (b) the
incremental estimate of confidence thresholds for the unknown classes, and (c)
the use of local learning to precisely describe the space of classes. We extend
three existing metric learning algorithms towards these goals by using online
metric learning. Experimentally we validate our approach on two large-scale
datasets in different learning scenarios. For all these scenarios our proposed
methods outperform their non-online counterparts. We conclude that local and
online learning is important to capture the full dynamics of open world
recognition.
