Structural Learning from Iconic Representations

Herman Martins GomesRobert B. Fisher

This paper addresses the important problem of how to learn geometric relationships from sets of iconic (2-D) models obtained from a sequence of images. It assumes a vision system that operates by foveating at interesting regions in a scene, extracting a number of raw primal sketch-like image descriptions, and matching new regions to previously seen ones. A solution to the structure learning problem is presented in terms of a graph-based representation and algorithm. Vertices represent instances of an image neighbourhood found in the scenes. An edge represents a relationship between two neighbourhoods. Intra and inter model relationships are inferred by means of the cliques found in the graph, which leads to rigid geometric models inferred from the image evidence.

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