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Abstract

A computer module is interactively trained to recognize patterns of spectral or textural features in imagery. The interactively trained module is based on techniques of knowledge-based image processing and an approach that uses interest images to provide a means of continuous feedback of module performance to the user. The user, in turn, responds by indicating where the module is making mistakes. A set of user indicated examples and counter examples form the inputs to a machine learning program called Functional Template Learning. The trained module is exportable as an independent agent to search large databases for matching patterns. Following retrieval of images having matching patterns, a user can further refine the agent's performance by indicating where mistakes have been made. The overall search tool employing the trained module is capable of prescreening and highlighting images, significantly reducing the workload of analysts attempting to detect regions or objects in imagery.

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First Claim