  In this paper, we investigate a new framework for image classification that
adaptively generates spatial representations. Our strategy is based on a
sequential process that learns to explore the different regions of any image in
order to infer its category. In particular, the choice of regions is specific
to each image, directed by the actual content of previously selected
regions.The capacity of the system to handle incomplete image information as
well as its adaptive region selection allow the system to perform well in
budgeted classification tasks by exploiting a dynamicly generated
representation of each image. We demonstrate the system's abilities in a series
of image-based exploration and classification tasks that highlight its learned
exploration and inference abilities.
