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公开(公告)号：US09710447B2
公开(公告)日：2017-07-18
申请号：US14215925
申请日：2014-03-17
Applicant: YAHOO! INC.
Inventor： Jia Li , Xiangnan Kong
CPC classification number: G06F17/241 , G06F17/30598 , G06K9/00677 , G06K9/6296
Abstract: System, method and architecture for providing improved visual recognition by modeling visual content, semantic content and an implicit social network representing individuals depicted in a collection of content, such as visual images, photographs, etc. which network may be determined based on co-occurrences of individuals represented by the content, and/or other data linking the individuals. In accordance with one or more embodiments, using images as an example, a relationship structure may comprise an implicit structure, or network, determined from co-occurrences of individuals in the images. A kernel jointly modeling content, semantic and social network information may be built and used in automatic image annotation and/or determination of relationships between individuals, for example.
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2.发明申请SYSTEM AND METHOD FOR LARGE-SCALE MULTI-LABEL LEARNING USING INCOMPLETE LABEL ASSIGNMENTS 审中-公开使用不完整的标签分配进行大规模多标签学习的系统和方法公开(公告)号：US20160140451A1
公开(公告)日：2016-05-19
申请号：US14543133
申请日：2014-11-17
Applicant: YAHOO! INC.
Inventor： Jia Li , Yi Chang , Xiangnan Kong
CPC classification number: G06N99/005
Abstract: At least one label prediction model is trained, or learned, using training data that may comprise training instances that may be missing one or more labels. The at least one label prediction model may be used in identifying a content item's ground-truth label set comprising an indicator for each label in the label set indicating whether or not the label is applicable to the content item.
Abstract translation: 使用可能包含可能缺少一个或多个标签的训练实例的训练数据来训练或学习至少一个标签预测模型。 所述至少一个标签预测模型可以用于识别内容项目的地面真相标签集合，其中包括标签组中的每个标签的指示符，指示标签是否适用于内容项目。
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公开(公告)号：US20150262037A1
公开(公告)日：2015-09-17
申请号：US14215925
申请日：2014-03-17
Applicant: YAHOO! INC.
Inventor： Jia Li , Xiangnan Kong
CPC classification number: G06F17/241 , G06F17/30598 , G06K9/00677 , G06K9/6296
Abstract: System, method and architecture for providing improved visual recognition by modeling visual content, semantic content and an implicit social network representing individuals depicted in a collection of content, such as visual images, photographs, etc. which network may be determined based on co-occurrences of individuals represented by the content, and/or other data linking the individuals. In accordance with one or more embodiments, using images as an example, a relationship structure may comprise an implicit structure, or network, determined from co-occurrences of individuals in the images. A kernel jointly modeling content, semantic and social network information may be built and used in automatic image annotation and/or determination of relationships between individuals, for example.
Abstract translation: 系统，方法和架构，用于通过建模视觉内容，语义内容和表示在诸如视觉图像，照片等的内容集合中描绘的个人的隐含的社交网络来提供改进的视觉识别。该网络可以基于共同事件来确定 由内容代表的个人和/或连接个人的其他数据。 根据一个或多个实施例，使用图像作为示例，关系结构可以包括由图像中的个体的共同出现确定的隐式结构或网络。 联合建模内容，语义和社交网络信息的内核可以被构建并用于例如自动图像注释和/或个体之间的关系的确定。
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