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    • 21. 发明申请
    • Multiple Category Learning for Training Classifiers
    • 训练分类器的多类学习
    • US20110119210A1
    • 2011-05-19
    • US12618799
    • 2009-11-16
    • Cha ZhangZhengyou Zhang
    • Cha ZhangZhengyou Zhang
    • G06F15/18
    • G06N99/005
    • Described is multiple category learning to jointly train a plurality of classifiers in an iterative manner. Each training iteration associates an adaptive label with each training example, in which during the iterations, the adaptive label of any example is able to be changed by the subsequent reclassification. In this manner, any mislabeled training example is corrected by the classifiers during training. The training may use a probabilistic multiple category boosting algorithm that maintains probability data provided by the classifiers, or a winner-take-all multiple category boosting algorithm selects the adaptive label based upon the highest probability classification. The multiple category boosting training system may be coupled to a multiple instance learning mechanism to obtain the training examples. The trained classifiers may be used as weak classifiers that provide a label used to select a deep classifier for further classification, e.g., to provide a multi-view object detector.
    • 描述了多类学习,以迭代的方式联合训练多个分类器。 每个训练迭代将自适应标签与每个训练示例相关联,其中在迭代期间,任何示例的自适应标签能够由随后的重新分类改变。 以这种方式,任何错误标记的训练示例在训练期间由分类器校正。 训练可以使用维护由分类器提供的概率数据的概率多类别提升算法,或者获胜者全部多类别增强算法基于最高概率分类来选择自适应标签。 多类别增强训练系统可以耦合到多实例学习机制以获得训练示例。 经训练的分类器可以用作弱分类器,其提供用于选择用于进一步分类的深分类器的标签,例如提供多视图对象检测器。
    • 29. 发明授权
    • Multi-device capture and spatial browsing of conferences
    • 会议的多设备捕获和空间浏览
    • US08537196B2
    • 2013-09-17
    • US12245774
    • 2008-10-06
    • Rajesh K. HegdeZhengyou ZhangPhilip A. ChouCha ZhangZicheng LiuSasa Junuzovic
    • Rajesh K. HegdeZhengyou ZhangPhilip A. ChouCha ZhangZicheng LiuSasa Junuzovic
    • H04N7/14G06F15/16G06F3/48
    • H04N7/157H04N7/147
    • Multi-device capture and spatial browsing of conferences is described. In one implementation, a system detects cameras and microphones, such as the webcams on participants' notebook computers, in a conference room, group meeting, or table game, and enlists an ad-hoc array of available devices to capture each participant and the spatial relationships between participants. A video stream composited from the array is browsable by a user to navigate a 3-dimensional representation of the meeting. Each participant may be represented by a video pane, a foreground object, or a 3-D geometric model of the participant's face or body displayed in spatial relation to the other participants in a 3-dimensional arrangement analogous to the spatial arrangement of the meeting. The system may automatically re-orient the 3-dimensional representation as needed to best show the currently interesting event such as current speaker or may extend navigation controls to a user for manually viewing selected participants or nuanced interactions between participants.
    • 描述会议的多设备捕获和空间浏览。 在一个实现中,系统检测相机和麦克风,例如参与者的笔记本计算机上的网络摄像机,会议室,组会议或桌面游戏,并且招募可用设备的特设阵列以捕获每个参与者和空间 参与者之间的关系。 从阵列合成的视频流可由用户浏览以浏览会议的三维表示。 每个参与者可以以类似于会议的空间安排的三维布置的视频窗格,前景对象或与其他参与者以空间关系显示的三维几何模型来表示。 该系统可以根据需要自动重新定向三维表示,以最佳地显示当前有趣的事件,例如当前的扬声器,或者可以将导航控件扩展到用户,以便手动地观看选定的参与者或参与者之间微妙的交互。