  A major goal of computer vision is to enable computers to interpret visual
situations---abstract concepts (e.g., "a person walking a dog," "a crowd
waiting for a bus," "a picnic") whose image instantiations are linked more by
their common spatial and semantic structure than by low-level visual
similarity. In this paper, we propose a novel method for prior learning and
active object localization for this kind of knowledge-driven search in static
images. In our system, prior situation knowledge is captured by a set of
flexible, kernel-based density estimations---a situation model---that represent
the expected spatial structure of the given situation. These estimations are
efficiently updated by information gained as the system searches for relevant
objects, allowing the system to use context as it is discovered to narrow the
search.
  More specifically, at any given time in a run on a test image, our system
uses image features plus contextual information it has discovered to identify a
small subset of training images---an importance cluster---that is deemed most
similar to the given test image, given the context. This subset is used to
generate an updated situation model in an on-line fashion, using an efficient
multipole expansion technique.
  As a proof of concept, we apply our algorithm to a highly varied and
challenging dataset consisting of instances of a "dog-walking" situation. Our
results support the hypothesis that dynamically-rendered, context-based
probability models can support efficient object localization in visual
situations. Moreover, our approach is general enough to be applied to diverse
machine learning paradigms requiring interpretable, probabilistic
representations generated from partially observed data.
