Biologically inspired architecture for the identification of ambiguous objects using scene associations

As humans, we have an excellent performance when perceiving the environment. In the artificial world, it is important for machines to perceive their environment so they can make correct decisions and act accordingly. An essential process to accomplish perception is to identify objects in a scene, but, as in reality, these objects can appear as ambiguous, and additionally, those objects are embedded into a particular scene. For our proposal, we created an architecture to identify ambiguous objects by using scene information to guide the identification process. The design is based on the human cortical systems that participate in object and scene recognition. In our study, we validate this proposal by analyzing a prior human experiment that demonstrates and quantifies the impact of scene information on ambiguous objects. Our findings demonstrate that employing the presented architecture on an object recognition task results in superior machine performance with familiar scenes, as opposed to unfamiliar or absent ones, consistent with human behavior.