  Sketch recognition allows natural and efficient interaction in pen-based
interfaces. A key obstacle to building accurate sketch recognizers has been the
difficulty of creating large amounts of annotated training data. Several
authors have attempted to address this issue by creating synthetic data, and by
building tools that support efficient annotation. Two prominent sets of
approaches stand out from the rest of the crowd. They use interim classifiers
trained with a small set of labeled data to aid the labeling of the remainder
of the data. The first set of approaches uses a classifier trained with a
partially labeled dataset to automatically label unlabeled instances. The
others, based on active learning, save annotation effort by giving priority to
labeling informative data instances. The former is sub-optimal since it doesn't
prioritize the order of labeling to favor informative instances, while the
latter makes the strong assumption that unlabeled data comes in an already
segmented form (i.e. the ink in the training data is already assembled into
groups forming isolated object instances). In this paper, we propose an active
learning framework that combines the strengths of these methods, while
addressing their weaknesses. In particular, we propose two methods for deciding
how batches of unsegmented sketch scenes should be labeled. The first method,
scene-wise selection, assesses the informativeness of each drawing (sketch
scene) as a whole, and asks the user to annotate all objects in the drawing.
The latter, segment-wise selection, attempts more precise targeting to locate
informative fragments of drawings for user labeling. We show that both
selection schemes outperform random selection. Furthermore, we demonstrate that
precise targeting yields superior performance. Overall, our approach allows
reaching top accuracy figures with up to 30% savings in annotation cost.
