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Abstract

A method for automatically generating a knowledge database in an object classification system having a digital image data source, and a computer, includes the steps of inputting digital image data corresponding to a plurality of training images, and characterizing the digital image data according to pre-defined variables, or descriptors, to thereby provide a plurality of descriptor vectors corresponding to the training images. Predetermined classification codes are inputted for the plurality of training images, to thereby define object class clusters comprising descriptor vector points having the same classification codes in N-dimensional Euclidean space. The descriptor vectors, or points, are reduced using a similarity matrix indicating proximity in N-dimensional Euclidean space, to select those descriptors vectors, called extreme points, which lie on the boundary surface of their respective class cluster. The non-selected points interior to the class cluster are not included in the knowledge database. The extreme points are balanced by eliminating functionally redundant extreme points from each class cluster to provide a preliminary knowledge database. Fine tuning of the preliminary knowledge database is performed by either deleting extreme points that tend to reduce the accuracy of the database, or adding new rules which enhance the accuracy of the data base. Alternately, the fine tuning step may be skipped.

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First Claim