  Scene-understanding is an important topic in the area of Computer Vision, and
illustrates computational challenges with applications to a wide range of
domains including remote sensing, surveillance, smart agriculture, robotics,
autonomous driving, and smart cities. We consider the active explanation-driven
understanding and classification of scenes. Suppose that an agent utilizing one
or more sensors is placed in an unknown environment, and based on its sensory
input, the agent needs to assign some label to the perceived scene. The agent
can adjust its sensor(s) to capture additional details about the scene, but
there is a cost associated with sensor manipulation, and as such, it is
important for the agent to understand the scene in a fast and efficient manner.
It is also important that the agent understand not only the global state of a
scene (e.g., the category of the scene or the major events taking place in the
scene) but also the characteristics/properties of the scene that support
decisions and predictions made about the global state of the scene. Finally,
when the agent encounters an unknown scene category, it must be capable of
refusing to assign a label to the scene, requesting aid from a human, and
updating its underlying knowledge base and machine learning models based on
feedback provided by the human. We introduce a dynamic data driven framework
for the active explanation-driven classification of scenes. Our framework is
entitled ACUMEN: Active Classification and Understanding Method by
Explanation-driven Networks. To demonstrate the utility of the proposed ACUMEN
approach and show how it can be adapted to a domain-specific application, we
focus on an example case study involving the classification of indoor scenes
using an active robotic agent with vision-based sensors, i.e., an
electro-optical camera.
