Feature-Driven Emergence of Model Graphs for Object Recognition and Categorization

Summary. An important requirement for the expression of cognitive structures is the ability to form mental objects by rapidly binding together constituent parts. In this sense, one may conceive the brain’s data structure to have the form of graphs whose nodes are labeled with elementary features. These provide a versatile data format with the ability to render the structure of any mental object. Because of the multitude of possible object variations the graphs are required to be dynamic. Upon presentation of an image a so-called model graph should rapidly emerge by binding together memorized subgraphs derived from earlier learning examples driven by the image features. In this model, the richness and flexibility of the mind is made possible by a combinatorial game of immense complexity. Consequently, emergence of model graphs is a laborious task which, in computer vision, has most often been disregarded in favor of employing model graphs tailored to specific object categories like faces in frontal pose. Invariant recognition or categorization of arbitrary objects, however, demands dynamic graphs. In this work we propose a form of graph dynamics which proceeds in three steps. I