  We model the process of human full interpretation of object images, namely
the ability to identify and localize all semantic features and parts that are
recognized by human observers. The task is approached by dividing the
interpretation of the complete object to the interpretation of multiple reduced
but interpretable local regions. We model interpretation by a structured
learning framework, in which there are primitive components and relations that
play a useful role in local interpretation by humans. To identify useful
components and relations used in the interpretation process, we consider the
interpretation of minimal configurations, namely reduced local regions that are
minimal in the sense that further reduction will turn them unrecognizable and
uninterpretable. We show experimental results of our model, and results of
predicting and testing relations that were useful to the model via transformed
minimal images.
