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1. 发明授权

US08086549B2 Multi-label active learning 有权
标题翻译：多标签主动学习
公开(公告)号：US08086549B2
公开(公告)日：2011-12-27
申请号：US11958050
申请日：2007-12-17
申请人： Guo-Jun Qi , Xian-Sheng Hua , Yong Rui , Hong-Jiang Zhang , Shipeng Li
发明人： Guo-Jun Qi , Xian-Sheng Hua , Yong Rui , Hong-Jiang Zhang , Shipeng Li
IPC分类号： G06F15/18
CPC分类号： G06N99/005
摘要： Multi-label active learning may entail training a classifier with a set of training samples having multiple labels per sample. In an example embodiment, a method includes accepting a set of training samples, with the set of training samples having multiple respective samples that are each respectively associated with multiple labels. The set of training samples is analyzed to select a sample-label pair responsive to at least one error parameter. The selected sample-label pair is then submitted to an oracle for labeling.
摘要翻译：多标签主动学习可能需要对分类器训练一组具有每个样本的多个标签的训练样本。在示例实施例中，一种方法包括接受一组训练样本，其中该组训练样本具有多个相应样本，每个样本分别与多个标签相关联。分析该组训练样本以响应于至少一个误差参数来选择样本标签对。然后将选定的样品标签对提交给oracle进行标记。

2. 发明申请

US20100076923A1 ONLINE MULTI-LABEL ACTIVE ANNOTATION OF DATA FILES 审中-公开
标题翻译：在线多标签数据文件的主动注释
公开(公告)号：US20100076923A1
公开(公告)日：2010-03-25
申请号：US12238290
申请日：2008-09-25
申请人： Xian-Sheng Hua , Guo-Jun Qi , Shipeng Li
发明人： Xian-Sheng Hua , Guo-Jun Qi , Shipeng Li
IPC分类号： G06F15/18 , G06Q30/00
CPC分类号： G06F16/70 , G06N20/00
摘要： Online multi-label active annotation may include building a preliminary classifier from a pre-labeled training set included with an initial batch of annotated data samples, and selecting a first batch of sample-label pairs from the initial batch of annotated data samples. The sample-label pairs may be selected by using a sample-label pair selection module. The first batch of sample-label pairs may be provided to online participants to manually annotate the first batch of sample-label pairs based on the preliminary classifier. The preliminary classifier may be updated to form a first updated classifier based on an outcome of the providing the first batch of sample-label pairs to the online participants.
摘要翻译：在线多标签活动注释可以包括从包括在初始批注的数据样本中的预先标记的训练集构建初步分类器，以及从初始批注的数据样本中选择第一批样本标签对。可以通过使用样本 - 标签对选择模块来选择样本 - 标签对。可以将第一批样本标签对提供给在线参与者，以基于初步分类器手动注释第一批样品 - 标签对。可以基于向在线参与者提供第一批样本标签对的结果来更新初步分类器以形成第一更新分类器。

3. 发明申请

US20100074537A1 KERNELIZED SPATIAL-CONTEXTUAL IMAGE CLASSIFICATION 有权
标题翻译：识别空间 - 上下文图像分类
公开(公告)号：US20100074537A1
公开(公告)日：2010-03-25
申请号：US12237298
申请日：2008-09-24
申请人： Xian-Sheng Hua , Guo-Jun Qi , Yong Rui , Hong-Jiang Zhang
发明人： Xian-Sheng Hua , Guo-Jun Qi , Yong Rui , Hong-Jiang Zhang
IPC分类号： G06K9/62
CPC分类号： G06K9/469 , G06K9/6297
摘要： Kernelized spatial-contextual image classification is disclosed. One embodiment comprises generating a first spatial-contextual model to represent a first image, the first spatial-contextual model having a plurality of interconnected nodes arranged in a first pattern of connections with each node connected to at least one other node, generating a second spatial-contextual model to represent a second image using the first pattern of connections, and estimating the distance between corresponding nodes in the first spatial-contextual model and the second spatial-contextual model based on a relationship with adjacent connected nodes to determine a distance between the first image and the second image.
摘要翻译：公开了内核空间上下文图像分类。一个实施例包括生成第一空间上下文模型以表示第一图像，第一空间上下文模型具有以与连接到至少一个其他节点的每个节点连接的第一连接方式布置的多个互连节点，产生第二空间 - 使用所述第一连接模式来表示第二图像，以及基于与相邻连接节点的关系来估计所述第一空间 - 上下文模型中的对应节点与所述第二空间 - 上下文模型之间的距离，以确定所述第二图像之间的距离第一个图像和第二个图像。

4. 发明申请

US20090083010A1 Correlative Multi-Label Image Annotation 有权
标题翻译：相关多标签图像注释
公开(公告)号：US20090083010A1
公开(公告)日：2009-03-26
申请号：US12030616
申请日：2008-02-13
申请人： Guo-Jun Qi , Xian-Sheng Hua , Yong Rui , Hong-Jiang Zhang , Shipeng Li
发明人： Guo-Jun Qi , Xian-Sheng Hua , Yong Rui , Hong-Jiang Zhang , Shipeng Li
IPC分类号： G06F17/10
CPC分类号： G06F17/30799 , G06K9/00711
摘要： Correlative multi-label image annotation may entail annotating an image by indicating respective labels for respective concepts. In an example embodiment, a classifier is to annotate an image by implementing a labeling function that maps an input feature space and a label space to a combination feature vector. The combination feature vector models both features of individual ones of the concepts and correlations among the concepts.
摘要翻译：相关多标签图像注释可能需要通过针对相应概念指示相应的标签来注释图像。在示例实施例中，分类器是通过实现将输入特征空间和标签空间映射到组合特征向量的标记功能来注释图像。组合特征向量模拟各个概念的特征和概念之间的相关性。

5. 发明授权

US08131086B2 Kernelized spatial-contextual image classification 有权
标题翻译：内核空间上下文图像分类
公开(公告)号：US08131086B2
公开(公告)日：2012-03-06
申请号：US12237298
申请日：2008-09-24
申请人： Xian-Sheng Hua , Guo-Jun Qi , Yong Rui , Hong-Jiang Zhang
发明人： Xian-Sheng Hua , Guo-Jun Qi , Yong Rui , Hong-Jiang Zhang
IPC分类号： G06K9/68
CPC分类号： G06K9/469 , G06K9/6297
摘要： Kernelized spatial-contextual image classification is disclosed. One embodiment comprises generating a first spatial-contextual model to represent a first image, the first spatial-contextual model having a plurality of interconnected nodes arranged in a first pattern of connections with each node connected to at least one other node, generating a second spatial-contextual model to represent a second image using the first pattern of connections, and estimating the distance between corresponding nodes in the first spatial-contextual model and the second spatial-contextual model based on a relationship with adjacent connected nodes to determine a distance between the first image and the second image.
摘要翻译：公开了内核空间上下文图像分类。一个实施例包括生成第一空间上下文模型以表示第一图像，第一空间上下文模型具有以与连接到至少一个其他节点的每个节点连接的第一连接方式布置的多个互连节点，产生第二空间 - 使用所述第一连接模式来表示第二图像，以及基于与相邻连接节点的关系来估计所述第一空间 - 上下文模型中的对应节点与所述第二空间 - 上下文模型之间的距离，以确定所述第二图像之间的距离第一个图像和第二个图像。

6. 发明授权

US07996762B2 Correlative multi-label image annotation 有权
标题翻译：相关多标签图像注释
公开(公告)号：US07996762B2
公开(公告)日：2011-08-09
申请号：US12030616
申请日：2008-02-13
申请人： Guo-Jun Qi , Xian-Sheng Hua , Yong Rui , Hong-Jiang Zhang , Shipeng Li
发明人： Guo-Jun Qi , Xian-Sheng Hua , Yong Rui , Hong-Jiang Zhang , Shipeng Li
IPC分类号： G06F17/00
CPC分类号： G06F17/30799 , G06K9/00711
摘要： Correlative multi-label image annotation may entail annotating an image by indicating respective labels for respective concepts. In an example embodiment, a classifier is to annotate an image by implementing a labeling function that maps an input feature space and a label space to a combination feature vector. The combination feature vector models both features of individual ones of the concepts and correlations among the concepts.
摘要翻译：相关多标签图像注释可能需要通过针对相应概念指示相应的标签来注释图像。在示例实施例中，分类器是通过实现将输入特征空间和标签空间映射到组合特征向量的标记功能来注释图像。组合特征向量模拟各个概念的特征和概念之间的相关性。

7. 发明申请

US20090290802A1 CONCURRENT MULTIPLE-INSTANCE LEARNING FOR IMAGE CATEGORIZATION 审中-公开
标题翻译：一致的多元学习图像分类
公开(公告)号：US20090290802A1
公开(公告)日：2009-11-26
申请号：US12125057
申请日：2008-05-22
申请人： Xian-Sheng Hua , Guo-Jun Qi , Yong Rui , Tao Mei , Hong-Jiang Zhang
发明人： Xian-Sheng Hua , Guo-Jun Qi , Yong Rui , Tao Mei , Hong-Jiang Zhang
IPC分类号： G06K9/62
CPC分类号： G06K9/34
摘要： The concurrent multiple instance learning technique described encodes the inter-dependency between instances (e.g. regions in an image) in order to predict a label for a future instance, and, if desired the label for an image determined from the label of these instances. The technique, in one embodiment, uses a concurrent tensor to model the semantic linkage between instances in a set of images. Based on the concurrent tensor, rank-1 supersymmetric non-negative tensor factorization (SNTF) can be applied to estimate the probability of each instance being relevant to a target category. In one embodiment, the technique formulates the label prediction processes in a regularization framework, which avoids overfitting, and significantly improves a learning machine's generalization capability, similar to that in SVMs. The technique, in one embodiment, uses Reproducing Kernel Hilbert Space (RKHS) to extend predicted labels to the whole feature space based on the generalized representer theorem.
摘要翻译：所描述的并发多实例学习技术编码实例（例如，图像中的区域）之间的相互依赖性，以便预测将来实例的标签，以及如果需要，从这些实例的标签确定的图像的标签。在一个实施例中，该技术使用并发张量来对一组图像中的实例之间的语义联系进行建模。基于并发张量，可以应用秩1超对称非负张量因子分解（SNTF）来估计每个实例与目标类别相关的概率。在一个实施例中，该技术在正则化框架中制定标签预测过程，其避免过拟合，并且显着地提高学习机器的泛化能力，类似于SVM中的标准预测过程。在一个实施例中，该技术使用再生核希尔伯特空间（RKHS）来基于广义代表定理将预测标签扩展到整个特征空间。

8. 发明申请

US20090125461A1 Multi-Label Active Learning 有权
标题翻译：多标签主动学习
公开(公告)号：US20090125461A1
公开(公告)日：2009-05-14
申请号：US11958050
申请日：2007-12-17
申请人： Guo-Jun Qi , Xian-Sheng Hua , Yong Rui , Hong-Jiang Zhang , Shipeng Li
发明人： Guo-Jun Qi , Xian-Sheng Hua , Yong Rui , Hong-Jiang Zhang , Shipeng Li
IPC分类号： G06F15/18
CPC分类号： G06N99/005
摘要： Multi-label active learning may entail training a classifier with a set of training samples having multiple labels per sample. In an example embodiment, a method includes accepting a set of training samples, with the set of training samples having multiple respective samples that are each respectively associated with multiple labels. The set of training samples is analyzed to select a sample-label pair responsive to at least one error parameter. The selected sample-label pair is then submitted to an oracle for labeling.
摘要翻译：多标签主动学习可能需要对分类器训练一组具有每个样本的多个标签的训练样本。在示例实施例中，一种方法包括接受一组训练样本，其中该组训练样本具有多个相应样本，每个样本分别与多个标签相关联。分析该组训练样本以响应于至少一个误差参数来选择样本标签对。然后将选定的样品标签对提交给oracle进行标记。

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