Discrepancy Directed Model Acquisition for Adaptive Perceptual Systems

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 ..

Learning Image to Symbol Conversion

A common paradigm in object recognition is to extract symbolic and/or numeric features from an image as a preprocessing step for classification. The machine learning and pattern recognition communities have produced many techniques for classifying instances given such features. In contrast, learning to extract a distinguishing set of features that will lead to unambiguous instance classification has received comparatively little attention. We propose a learning paradigm that integrates feature extraction and classifier induction, exploiting their close interrelationship to give improved classification performance. Introduction Object recognition systems can conceptually be divided into two phases: feature extraction and recognition. In the feature extraction phase, feature vectors are extracted for each instance. In general, the features are hand selected, as are their parameters, for example the cut-off frequencies of a bandpass filter or the window size of a convolution operator. In..

A New Data Structure HC-Expression for Learning from Examples

A new data structure Hierarchical Counterfactual Expression (HC-Expression) is proposed.Its use in the area of learning from examples is studied. HC-Expression is a tree-like structure with alternate levels representing positive and negative exceptions to the rule. It is flexible and powerful enough to describe disjunctive concepts and can be visualised as a decision tree. Expressions to describe a concept can be efficiently generated from a set of examples and counter examples of a concept. The efficacy of the proposed method is examined by applying it to a set of data collected from the Institute of Indian Medicine