Discrepancy Directed Model Acquisition for Adaptive Perceptual Systems

Abstract

For complex perceptual tasks that are characterized by object occlusion and nonstationarity, recognition systems with adaptive signal processing front-ends have been developed. These systems rely on hand-crafted symbolic object models, which constitutes a knowledge acquisition bottleneck. We propose an approach to automate object model acquisition that relies on the detection and resolution of signal processing and interpretation discrepancies. The approach is applied to the task of acquiring acoustic-event models for the Sound Understanding Testbed (SUT). 1 Introduction To meet the challenge of recognition in environments that are characterized by varying signal-to-noise ratio, unpredictable object activity and possible object occlusion, Adaptive Perceptual Systems [ Draper, 1993; Lesser et al., 1993; Ming and Bhanu, 1990 ] have emerged. Recognition in such systems is dependent on the interaction between feature extraction and interpretation /matching: failure to account for some or ..