1. A method of actionable intelligence for detecting anomalous entities, comprising an act of initializing one or more processors to perform operations of: receiving an input signal; selecting a class of entities to be recognized; recognizing a set of entities of the selected class in the input signal using an Adaptive Resonance Theory (ART)-based neural network; selecting a set of threshold criteria by which a set of anomalous entities can be detected within the set of recognized entities; detecting the set of anomalous entities by comparing the set of recognized entities against the set of threshold criteria; alerting an operator to the presence of the set of anomalous entities, whereby anomalous entities are detected; prompting the operator to assign new labels to the set of anomalous entities; discovering underlying hierarchical relationships between the new labels assigned by the operator; and updating a knowledge database with the new labels and hierarchical relationships, whereby anomalous entities are classified and hierarchically related.
1. A method of actionable intelligence for detecting anomalous entities, comprising an act of initializing one or more processors to perform operations of: receiving an input signal; selecting a class of entities to be recognized; recognizing a set of entities of the selected class in the input signal using an Adaptive Resonance Theory (ART)-based neural network; selecting a set of threshold criteria by which a set of anomalous entities can be detected within the set of recognized entities; detecting the set of anomalous entities by comparing the set of recognized entities against the set of threshold criteria; alerting an operator to the presence of the set of anomalous entities, whereby anomalous entities are detected; prompting the operator to assign new labels to the set of anomalous entities; discovering underlying hierarchical relationships between the new labels assigned by the operator; and updating a knowledge database with the new labels and hierarchical relationships, whereby anomalous entities are classified and hierarchically related. 6. The method of claim 1 , wherein in recognizing the set of entities, the one or more processors further perform operations of: using a Scene Recognition Engine (SCE) to recognize spatial relationships between objects that compose a particular scene category in static imagery as captured from the input signal, where the input signal is a video signal; and using an Event Recognition Engine (ERE) to recognize spatio-temporal sequences and, thereby, recognize events and behaviors in the video signal by maintaining and recognizing ordered spatio-temporal sequences.