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One way of enforcing both speed and accuracy in feature subset selection is by attaching to each operation (e.g., computation of features, feature selection, labeling method), and each type of error, a cost. This paper exploits the concept of cost-based feature subset selection, and compares implementations based on different criteria (divergence criterion, Bhattacharyya distance). The novelty of this paper is the use of explicit, user-defined, cost limits, which affect the choice both of the feature subset selection mechanism and of the features.
Examples of GIS-based digitizing of both multispectral and panchromatic high resolution satellite imagery illustrate the usefulness of the proposed method in real-time, interactive image analysis.

Additional information

Authors: SMITS P C, AIS Unit;ANNONI A, AIS Unit
Bibliographic Reference: Paper presented: International Conference on Pattern Recognition (ICPR) 2000, Barcelona (ES), 3-7 September 2000
Availability: Space Applications Institute, Joint Research Centre, TP262 -I-21020 Ispra (Varese), IT
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