Abstract

To recognize an object in an image one must have an internal model of how that object may appear. We describe a method for learning such models from training images. An object is modeled by a probability distribution describing the range of possible variation in the object's appearance. This distribution is organized on two levels. Large variations are handled by partitioning the training images into clusters that correspond to distinctly different views of the object. Within each cluster, smaller variations are represented by distributions that characterize the presence, position, and measurements of various discrete features of appearance. The learning process combines an incremental conceptual clustering algorithm for forming the clusters with a generalization algorithm for consolidating each cluster's training images into a single description. Recognition employs information about feature positions, numeric measurements, and relations in order to constrain and speed the search. Pre...

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