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    • 5. 发明申请
    • Unbiased Active Learning
    • 无偏见主动学习
    • US20100217732A1
    • 2010-08-26
    • US12391511
    • 2009-02-24
    • Linjun YangBo GengXian-Sheng Hua
    • Linjun YangBo GengXian-Sheng Hua
    • G06F15/18
    • G06N99/005
    • Techniques described herein create an accurate active-learning model that takes into account a sample selection bias of elements, such as images, selected for labeling by a user. These techniques select a first set of elements for labeling. Once a user labels these elements, the techniques calculate a sample selection bias of the selected elements and train a model that takes into account the sample selection bias. The techniques then select a second set of elements based, in part, on a sample selection bias of the elements. Again, once a user labels the second set of elements the techniques train the model while taking into account the calculated sample selection bias. Once the trained model satisfies a predefined stop condition, the techniques use the trained model to predict labels for the remaining unlabeled elements.
    • 本文描述的技术创建了一种精确的主动学习模型,其考虑了由用户选择进行标签选择的元素(例如图像)的样本选择偏差。 这些技术选择用于标记的第一组元素。 一旦用户标记了这些元素,这些技术就会计算所选元素的样本选择偏差,并训练考虑样本选择偏倚的模型。 然后,技术部分地基于元素的样本选择偏差来选择第二组元素。 同样,一旦用户标记第二组元素,则该技术训练模型,同时考虑计算的样本选择偏差。 一旦训练的模型满足预定义的停止条件,该技术使用经过训练的模型来预测剩余的未标记元素的标签。
    • 7. 发明授权
    • Unbiased active learning
    • 不偏不倚的主动学习
    • US08219511B2
    • 2012-07-10
    • US12391511
    • 2009-02-24
    • Linjun YangBo GengXian-Sheng Hua
    • Linjun YangBo GengXian-Sheng Hua
    • G06F15/18G06F17/10G06N3/08
    • G06N99/005
    • Techniques described herein create an accurate active-learning model that takes into account a sample selection bias of elements, such as images, selected for labeling by a user. These techniques select a first set of elements for labeling. Once a user labels these elements, the techniques calculate a sample selection bias of the selected elements and train a model that takes into account the sample selection bias. The techniques then select a second set of elements based, in part, on a sample selection bias of the elements. Again, once a user labels the second set of elements the techniques train the model while taking into account the calculated sample selection bias. Once the trained model satisfies a predefined stop condition, the techniques use the trained model to predict labels for the remaining unlabeled elements.
    • 本文描述的技术创建了一种精确的主动学习模型,其考虑了由用户选择进行标签选择的元素(例如图像)的样本选择偏差。 这些技术选择用于标记的第一组元素。 一旦用户标记了这些元素,这些技术就会计算所选元素的样本选择偏差,并训练考虑样本选择偏倚的模型。 然后,技术部分地基于元素的样本选择偏差来选择第二组元素。 同样,一旦用户标记第二组元素,则该技术训练模型,同时考虑计算的样本选择偏差。 一旦训练的模型满足预定义的停止条件,该技术使用经过训练的模型来预测剩余的未标记元素的标签。
    • 9. 发明授权
    • Searching for images by video
    • 通过视频搜索图像
    • US09443011B2
    • 2016-09-13
    • US13110708
    • 2011-05-18
    • Linjun YangXian-Sheng HuaYang Cai
    • Linjun YangXian-Sheng HuaYang Cai
    • G06K9/46G06F17/30
    • G06K9/4676G06F17/30796G06F17/30799G06K9/00671G06K9/00744G06K9/38G06K9/4642G06K9/4671G06K9/623
    • Techniques describe submitting a video clip as a query by a user. A process retrieves images and information associated with the images in response to the query. The process decomposes the video clip into a sequence of frames to extract the features in a frame and to quantize the extracted features into descriptive words. The process further tracks the extracted features as points in the frame, a first set of points to correspond to a second set of points in consecutive frames to construct a sequence of points. Then the process identifies the points that satisfy criteria of being stable points and being centrally located in the frame to represent the video clip as a bag of descriptive words for searching for images and information related to the video clip.
    • 技术描述提交视频剪辑作为用户的查询。 响应于查询,进程检索与图像相关联的图像和信息。 该过程将视频剪辑分解成帧序列以提取帧中的特征并将提取的特征量化为描述性词。 该过程进一步跟踪提取的特征作为帧中的点,第一组点对应于连续帧中的第二组点以构成点序列。 然后,该过程识别满足稳定点的标准并且位于帧中心的点以将视频剪辑表示为用于搜索与视频剪辑相关的图像和信息的描述词的一袋。