Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Pascal Visual Object Classes (VOC) Challenge
International Journal of Computer Vision
PETS2010 and PETS2009 Evaluation of Results Using Individual Ground Truthed Single Views
AVSS '10 Proceedings of the 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance
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We present a novel method to maximize multiclass classifier performance by tuning the thresholds of the constituent pairwise binary classifiers using Particle Swarm Optimization. This post-processing step improves the classification performance in multiclass visual object detection by maximizing the area under the ROC curve or various operating points on the ROC curve. We argue that the precision-recall or confusion matrix commonly used for measuring the performance of multiclass visual object detection algorithms is inadequate to the Multiclass ROC when the intent is to apply the recognition algorithm for surveillance where objects remain in view for multiple consecutive frames, and where background instances exists in far greater numbers than target instances. We demonstrate its efficacy on the visual object detection problem with a 4-class classifier. Despite this, the PSO threshold tuning method can be applied to all pairwise multiclass classifiers using any computable performance metric.