1. A computer-implemented method for a video surveillance system to process a sequence of video frames depicting a scene captured by a video camera, comprising: receiving a request to view an object classification type assigned to a foreground object depicted in the sequence of video frames, wherein the object classification type classifies the foreground object as being an instance of one of a plurality of foreground object classification types, wherein the object classification type is assigned to the foreground object based on a plurality of micro-features derived from analyzing pixels depicting the foreground object in the sequence of video frames, wherein the object classification type is generated by mapping micro-features derived from a plurality of foreground objects to nodes of a self-organizing map (SOM) and wherein an Adaptive Resonance Theory (ART) network clusters resulting nodes in the SOM, and wherein each ART network cluster corresponds to one of the plurality of foreground classification types; generating a visual representation of the requested object classification type; outputting the generated visual representation for display; receiving user input requesting to modify a metadata attribute of the object classification type assigned to the foreground object; and modifying the metadata attribute of the object classification, based on the received user input.