  To identify the location of objects of a particular class, a passive computer
vision system generally processes all the regions in an image to finally output
few regions. However, we can use structure in the scene to search for objects
without processing the entire image. We propose a search technique that
sequentially processes image regions such that the regions that are more likely
to correspond to the query class object are explored earlier. We frame the
problem as a Markov decision process and use an imitation learning algorithm to
learn a search strategy. Since structure in the scene is essential for search,
we work with indoor scene images as they contain both unary scene context
information and object-object context in the scene. We perform experiments on
the NYU-depth v2 dataset and show that the unary scene context features alone
can achieve a significantly high average precision while processing only
20-25\% of the regions for classes like bed and sofa. By considering
object-object context along with the scene context features, the performance is
further improved for classes like counter, lamp, pillow and sofa.
