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1. 发明授权

US08588519B2 Method and system for training a landmark detector using multiple instance learning 有权
标题翻译：使用多实例学习训练地标探测器的方法和系统
公开(公告)号：US08588519B2
公开(公告)日：2013-11-19
申请号：US13228509
申请日：2011-09-09
申请人： David Liu , Shaohua Kevin Zhou , Paul Swoboda , Dorin Comaniciu , Christian Tietjen
发明人： David Liu , Shaohua Kevin Zhou , Paul Swoboda , Dorin Comaniciu , Christian Tietjen
IPC分类号： G06K9/52
CPC分类号： G06K9/6257 , G06K2209/051
摘要： An apparatus and method for training a landmark detector receives training data which includes a plurality of positive training bags, each including a plurality of positively annotated instances, and a plurality of negative training bags, each including at least one negatively annotated instance. Classification function is initialized by training a first weak classifier based on the positive training bags and the negative training bags. All training instances are evaluated using the classification function. For each of a plurality of remaining classifiers, a cost value gradient is calculated based on spatial context information of each instance in each positive bag evaluated by the classification function. A gradient value associated with each of the remaining weak classifiers is calculated based on the cost value gradients, and a weak classifier is selected which has a lowest associated gradient value and given a weighting parameter and added to the classification function.
摘要翻译：用于训练地标检测器的装置和方法接收训练数据，训练数据包括多个正训练袋，每个正训练袋包括多个带有正面注释的实例，以及多个负训练袋，每个包括至少一个负注释实例。基于积极的训练袋和负面训练袋训练第一个弱分类器来初始化分类功能。使用分类函数评估所有训练实例。对于多个剩余分类器中的每一个，基于由分类函数评估的每个正包中的每个实例的空间上下文信息来计算成本值梯度。基于成本值梯度计算与剩余弱分类器中的每一个相关联的梯度值，并且选择具有最低相关梯度值并给出加权参数并加到分类函数的弱分类器。

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