A hybrid feature selection and classification strategy was proposed based on the simulated annealing genetic algonthrn and multiple instance learning （MIL）. The band selection method was proposed from subspace decom...A hybrid feature selection and classification strategy was proposed based on the simulated annealing genetic algonthrn and multiple instance learning （MIL）. The band selection method was proposed from subspace decomposition, which combines the simulated annealing algorithm with the genetic algorithm in choosing different cross-over and mutation probabilities, as well as mutation individuals. Then MIL was combined with image segmentation, clustering and support vector machine algorithms to classify hyperspectral image. The experimental results show that this proposed method can get high classification accuracy of 93.13% at small training samples and the weaknesses of the conventional methods are overcome.展开更多
在公共安全领域,如何借助视频监控设备实现实时、高效的异常事件检测,已成为一个的重要研究课题。为此,本文提出一种基于隐式类激活特征和标签置信度的弱监督视频异常检测算法。针对正常与异常之间的界限模糊并会随着不同的场景而有所...在公共安全领域,如何借助视频监控设备实现实时、高效的异常事件检测,已成为一个的重要研究课题。为此,本文提出一种基于隐式类激活特征和标签置信度的弱监督视频异常检测算法。针对正常与异常之间的界限模糊并会随着不同的场景而有所变化的问题,提出使用隐式类激活模块差异化正常和异常的类间特征表达。针对多示例学习框架引入的标签噪声问题,采用基于标签置信度感知的自训练策略,通过计算伪标签的置信度,在模型迭代过程不断提高伪标签的质量。本文算法在ShanghaiTech和UCF-Crime这2个公开数据集上的曲线下面积(area under curve,AUC)分别达到97.63%和86.38%。模型在制造业工厂实际场景中进行测试,实验结果表明所提算法能够有效检测视频中的异常事件。展开更多
文摘A hybrid feature selection and classification strategy was proposed based on the simulated annealing genetic algonthrn and multiple instance learning （MIL）. The band selection method was proposed from subspace decomposition, which combines the simulated annealing algorithm with the genetic algorithm in choosing different cross-over and mutation probabilities, as well as mutation individuals. Then MIL was combined with image segmentation, clustering and support vector machine algorithms to classify hyperspectral image. The experimental results show that this proposed method can get high classification accuracy of 93.13% at small training samples and the weaknesses of the conventional methods are overcome.
文摘在公共安全领域,如何借助视频监控设备实现实时、高效的异常事件检测,已成为一个的重要研究课题。为此,本文提出一种基于隐式类激活特征和标签置信度的弱监督视频异常检测算法。针对正常与异常之间的界限模糊并会随着不同的场景而有所变化的问题,提出使用隐式类激活模块差异化正常和异常的类间特征表达。针对多示例学习框架引入的标签噪声问题,采用基于标签置信度感知的自训练策略,通过计算伪标签的置信度,在模型迭代过程不断提高伪标签的质量。本文算法在ShanghaiTech和UCF-Crime这2个公开数据集上的曲线下面积(area under curve,AUC)分别达到97.63%和86.38%。模型在制造业工厂实际场景中进行测试,实验结果表明所提算法能够有效检测视频中的异常事件。
