  Image and video retrieval by their semantic content has been an important and
challenging task for years, because it ultimately requires bridging the
symbolic/subsymbolic gap. Recent successes in deep learning enabled detection
of objects belonging to many classes greatly outperforming traditional computer
vision techniques. However, deep learning solutions capable of executing
retrieval queries are still not available. We propose a hybrid solution
consisting of a deep neural network for object detection and a cognitive
architecture for query execution. Specifically, we use YOLOv2 and OpenCog.
Queries allowing the retrieval of video frames containing objects of specified
classes and specified spatial arrangement are implemented.
