On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
CSB '02 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Moment-Based Pattern Representation Using Shape and Grayscale Features
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
Combining pattern recognition modalities at the sensor level via kernel fusion
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
A multilevel information fusion approach for visual quality inspection
Information Fusion
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When applying Machine Learning technology to real-world applications, such as visual quality inspection, several practical issues need to be taken care of. One problem is posed by the reality that usually there are multiple human operators doing the inspection, who will inevitable contradict each other occasionally. In this paper a framework is proposed which is able to deal with this issue, based on trained ensembles of classifiers. Most ensemble techniques have however difficulties learning in these circumstances. Therefore several novel enhancements to the Grading ensemble technique are proposed within this framework --- called Active Grading . The Active Grading algorithm is evaluated on data obtained from a real-world industrial system for visual quality inspection of the printing of labels on CDs, which was labelled independently by four different human operators and their supervisor, and compared to the standard Grading algorithm and a range of other ensemble (classifier fusion) techniques.