Combining the results of several neural network classifiers
Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Expert Systems: Uncertainty and Learning
Expert Systems: Uncertainty and Learning
An Evaluation of Grading Classifiers
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
An on-line interactive self-adaptive image classification framework
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
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In this work we propose a novel pairwise diversity measure, that recalls the Fisher linear discriminant, to construct a classifier ensemble for tracking a non-rigid object in a complex environment. A subset of constantly updated classifiers is selected exploiting their capability to distinguish the target from the background and, at the same time, promoting independent errors. This reduced ensemble is employed in the target search phase, speeding up the application of the system and maintaining the performance comparable to state of the art algorithms. Experiments have been conducted on a Pan-Tilt-Zoom camera video sequence to demonstrate the effectiveness of the proposed approach coping with pose variations of the target.