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Optimal multi-class classifier threshold-offset estimation with particle swarm optimization for visual object recognition
Optimal multi-class classifier threshold-offset estimation with particle swarm optimization for visual object recognition
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机译:最优的多类分类器阈值偏移估计与粒子群算法的视觉目标识别
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摘要
Described is a system for multi-class classifier threshold-offset estimation for visual object recognition. The system receives an input image with input features for classifying. A pair-wise classifier is trained for each pair of a plurality of object classes. A set of classification responses is generated, and a multi-class receiver-operating-characteristics (ROC) curve is computed for a set of threshold-offsets. An objective function of classification performance is computed from the ROC curve and optimized using particle swarm optimization (PSO) to generate a set of optimized threshold-offsets. The optimized threshold-offsets are then applied to the classification responses. The resulting classification responses are compared to a predetermined value to classify each input feature as belonging to one object class or another. The tuning of the threshold-offsets with (PSO) improves classification performance in a visual object recognition system.
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