The goal in this work is to model the process of  ‘full interpretation’  of  object images,  which is 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. In such reduced regions, interpretation is  simpler,  since the number of  se mantic  components is small, and the variability of possible configurations is low. 

We model the interpretation process by identifying primitive components and  relations that play a useful role in  local  interpretation by humans. To identify useful  compo nents and relations used in the interpretation process, we consider the  interpretation of  ‘ minimal configurations’ :  these  are  reduced  local regions , whic h are  minimal in the sense that further reduction  renders them unrecognizable and  uninterpretable.  We show that  such  minimal  interpretable image s have useful properties,  which  we use to identify  informative  features and relations used for full interpretation.  We describe our interpretation model, and show results of  detailed  interpretations  of  minimal c onfigurations, produced automatically by the model. Finally, we  discuss  implications of  full  interpretation  to  difficult visual tasks, such as recognizing human  activities or interactions , which are beyond the scope of current models of visual  recognition .