  Object recognition and localization are important tasks in computer vision.
The focus of this work is the incorporation of contextual information in order
to improve object recognition and localization. For instance, it is natural to
expect not to see an elephant to appear in the middle of an ocean. We consider
a simple approach to encapsulate such common sense knowledge using
co-occurrence statistics from web documents. By merely counting the number of
times nouns (such as elephants, sharks, oceans, etc.) co-occur in web
documents, we obtain a good estimate of expected co-occurrences in visual data.
We then cast the problem of combining textual co-occurrence statistics with the
predictions of image-based classifiers as an optimization problem. The
resulting optimization problem serves as a surrogate for our inference
procedure. Albeit the simplicity of the resulting optimization problem, it is
effective in improving both recognition and localization accuracy. Concretely,
we observe significant improvements in recognition and localization rates for
both ImageNet Detection 2012 and Sun 2012 datasets.
