论文标题
在线对象识别和重新识别任务的自适应阈值
Adaptive Threshold for Online Object Recognition and Re-identification Tasks
论文作者
论文摘要
选择决策门槛是任何分类任务中具有挑战性的工作之一。该模型是准确的，如果不仔细地决定边界，其整个性能将徒劳地进行。另一方面，对于不平衡的分类，其中一个班级占主导地位，依赖于选择阈值的常规方法将导致性能差。即使基于SVM和决策树等机器学习策略正确选择了阈值或决策边界，它在某个时候会在某个时候进行动态变化的数据库，并且如果具有或多或少相似的身份功能，例如面部识别和人员重新识别模型。因此，由于需要适应性阈值选择不平衡的分类和增量数据库大小，因此在LFW数据集和自我准备的运动员数据集中开发和测试了基于在线优化的统计功能学习自适应技术。这种采用自适应阈值的方法与固定阈值{0.3,0.5,0.7}相比，模型准确性提高了12-45％，这些阈值通常通过任何分类和识别任务中的命中和判断方法采用。完整算法的源代码可在以下网址获得：https：//github.com/varat7v2/adaptive-threshold
Choosing a decision threshold is one of the challenging job in any classification tasks. How much the model is accurate, if the deciding boundary is not picked up carefully, its entire performance would go in vain. On the other hand, for imbalance classification where one of the classes is dominant over another, relying on the conventional method of choosing threshold would result in poor performance. Even if the threshold or decision boundary is properly chosen based on machine learning strategies like SVM and decision tree, it will fail at some point for dynamically varying databases and in case of identity-features that are more or less similar, like in face recognition and person re-identification models. Hence, with the need for adaptability of the decision threshold selection for imbalanced classification and incremental database size, an online optimization-based statistical feature learning adaptive technique is developed and tested on the LFW datasets and self-prepared athletes datasets. This method of adopting adaptive threshold resulted in 12-45% improvement in the model accuracy compared to the fixed threshold {0.3,0.5,0.7} that are usually taken via the hit-and-trial method in any classification and identification tasks. Source code for the complete algorithm is available at: https://github.com/Varat7v2/adaptive-threshold
