Classification quality assessment for a generalized model-based object identification system

An object recognition system based on global features and nearest neighbor matching is extended and enhanced using classification quality assessment methodology. For quality assessment, a classification decision is processed at two levels. The first is to reject options that are not contained in the model database. The second is to identify the likelihood of error for classifications of known objects. Both stages are based on empirically determined thresholds of measures that are generated solely from the system's a priori knowledge, exploiting the known characteristics of both physical object space and feature space. Results are presented for a standardized object identification task, with a set of six similar known objects, and four unknown objects. It is shown that objects outside the model database are effectively rejected and that the accuracy of known object identifications is increased by rejecting views of low classification quality.<
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