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11. 发明申请

US20090310854A1 Multi-Label Multi-Instance Learning for Image Classification 有权
标题翻译：图像分类的多标签多实例学习
公开(公告)号：US20090310854A1
公开(公告)日：2009-12-17
申请号：US12140247
申请日：2008-06-16
申请人： Tao Mei , Xian-Sheng Hua , Shipeng Li , Zheng-Jun Zha
发明人： Tao Mei , Xian-Sheng Hua , Shipeng Li , Zheng-Jun Zha
IPC分类号： G06K9/62
CPC分类号： G06K9/4638 , G06K9/342
摘要： Described is a technology by which an image is classified (e.g., grouped and/or labeled), based on multi-label multi-instance data learning-based classification according to semantic labels and regions. An image is processed in an integrated framework into multi-label multi-instance data, including region and image labels. The framework determines local association data based on each region of an image. Other multi-label multi-instance data is based on relationships between region labels of the image, relationships between image labels of the image, and relationships between the region and image labels. These data are combined to classify the image. Training is also described.
摘要翻译：基于根据语义标签和区域的基于多标签多实例数据学习的分类，描述了图像被分类（例如，分组和/或标记）的技术。图像在集成框架中被处理成多标签多实例数据，包括区域和图像标签。该框架基于图像的每个区域确定局部关联数据。其他多标签多实例数据基于图像的区域标签之间的关系，图像的图像标签之间的关系以及区域和图像标签之间的关系。组合这些数据以对图像进行分类。培训也被描述。

12. 发明授权

US09271035B2 Detecting key roles and their relationships from video 有权
标题翻译：从视频中检测关键角色及其关系
公开(公告)号：US09271035B2
公开(公告)日：2016-02-23
申请号：US13085288
申请日：2011-04-12
申请人： Tao Mei , Xian-Sheng Hua , Shipeng Li , Yan Wang
发明人： Tao Mei , Xian-Sheng Hua , Shipeng Li , Yan Wang
IPC分类号： H04N21/44 , G06Q30/02 , H04N21/84 , G06K9/00
CPC分类号： H04N21/44008 , G06F17/30793 , G06F17/30843 , G06K9/00718 , G06Q30/0276 , H04N21/84
摘要： Tools and techniques for acquiring key roles and their relationships from a video independent of metadata, such as cast lists and scripts, are described herein. These techniques include discovering key roles and their relationships by treating a video (e.g., a movie, television program, music video, and personal video, etc.) as a community. For instance, a video is segmented into a hierarchical structure that includes levels for scenes, shots, and key frames. In some implementations, the techniques include performing face detection and grouping on the detected key frames. In some implementations, the techniques include exploiting the key roles and their correlations in this video to discover a community. The discovered community provides for a wide variety of applications, including the automatic generation of visual summaries or video posters including acquired key roles.
摘要翻译：本文描述了从独立于元数据的视频（如演员列表和脚本）获取关键角色及其关系的工具和技术。这些技术包括通过将视频（例如，电影，电视节目，音乐视频和个人视频等）视为社区来发现关键角色及其关系。例如，视频被分割成层次结构，其包括场景，镜头和关键帧的级别。在一些实现中，这些技术包括在检测到的关键帧上执行面部检测和分组。在一些实现中，这些技术包括利用该视频中的关键角色及其相关性来发现社区。被发现的社区提供了广泛的应用，包括自动生成视觉摘要或视频海报，包括已获得的关键角色。

13. 发明申请

US20120297038A1 Recommendations for Social Network Based on Low-Rank Matrix Recovery 审中-公开
标题翻译：基于低阶矩阵恢复的社会网络建议
公开(公告)号：US20120297038A1
公开(公告)日：2012-11-22
申请号：US13108843
申请日：2011-05-16
申请人： Tao Mei , Xian-Sheng Hua , Shipeng Li , Jinfeng Zhuang
发明人： Tao Mei , Xian-Sheng Hua , Shipeng Li , Jinfeng Zhuang
IPC分类号： G06F15/173 , G06F17/30
CPC分类号： G06Q50/01
摘要： Techniques describe analyzing users and groups of a social network to identify user interests and providing recommendations for a user based on the user's identified interests. A content-awareness application obtains a collection of images and tags associated with the images belonging to members in the social network. The content-awareness application decomposes the members into a representative matrix to identify users and groups in order to calculate a similarity matrix between the users and their images based on a visual content of the images and a textual content of the tags. The content-awareness application further constructs a graph Laplacian over the users and the groups to align with the representative matrix based at least in part on the similarity matrix and further provides recommendations of groups for a user to join in the social network based at least in part on the graph Laplacian identifying the user's interests.
摘要翻译：技术描述了分析社交网络的用户和组以识别用户兴趣并基于用户识别的兴趣为用户提供建议。内容感知应用程序获得与属于社交网络中的成员的图像相关联的图像和标签的集合。内容感知应用程序将成员分解为代表性矩阵以识别用户和组，以便基于图像的可视内容和标签的文本内容来计算用户和他们的图像之间的相似性矩阵。该内容感知应用进一步构建一个关于用户和组的拉普拉斯算子，以至少部分地基于相似性矩阵与代表性矩阵一致，并进一步提供用户群体的建议，以便用户至少在拉普拉斯确定用户兴趣的一部分。

14. 发明申请

US20120263433A1 Detecting Key Roles and Their Relationships from Video 有权
标题翻译：从视频中检测关键角色及其关系
公开(公告)号：US20120263433A1
公开(公告)日：2012-10-18
申请号：US13085288
申请日：2011-04-12
申请人： Tao Mei , Xian-Sheng Hua , Shipeng Li , Yan Wang
发明人： Tao Mei , Xian-Sheng Hua , Shipeng Li , Yan Wang
IPC分类号： H04N9/80
CPC分类号： H04N21/44008 , G06F17/30793 , G06F17/30843 , G06K9/00718 , G06Q30/0276 , H04N21/84
摘要： Tools and techniques for acquiring key roles and their relationships from a video independent of metadata, such as cast lists and scripts, are described herein. These techniques include discovering key roles and their relationships by treating a video (e.g., a movie, television program, music video, and personal video, etc.) as a community. For instance, a video is segmented into a hierarchical structure that includes levels for scenes, shots, and key frames. In some implementations, the techniques include performing face detection and grouping on the detected key frames. In some implementations, the techniques include exploiting the key roles and their correlations in this video to discover a community. The discovered community provides for a wide variety of applications, including the automatic generation of visual summaries or video posters including acquired key roles.
摘要翻译：本文描述了从独立于元数据的视频（如演员列表和脚本）获取关键角色及其关系的工具和技术。这些技术包括通过将视频（例如，电影，电视节目，音乐视频和个人视频等）视为社区来发现关键角色及其关系。例如，视频被分割成层次结构，其包括场景，镜头和关键帧的级别。在一些实现中，这些技术包括在检测到的关键帧上执行面部检测和分组。在一些实现中，这些技术包括利用该视频中的关键角色及其相关性来发现社区。被发现的社区提供了广泛的应用，包括自动生成视觉摘要或视频海报，包括已获得的关键角色。

15. 发明授权

US08249366B2 Multi-label multi-instance learning for image classification 有权
标题翻译：用于图像分类的多标签多实例学习
公开(公告)号：US08249366B2
公开(公告)日：2012-08-21
申请号：US12140247
申请日：2008-06-16
申请人： Tao Mei , Xian-Sheng Hua , Shipeng Li , Zheng-Jun Zha
发明人： Tao Mei , Xian-Sheng Hua , Shipeng Li , Zheng-Jun Zha
IPC分类号： G06K9/00
CPC分类号： G06K9/4638 , G06K9/342
摘要： Described is a technology by which an image is classified (e.g., grouped and/or labeled), based on multi-label multi-instance data learning-based classification according to semantic labels and regions. An image is processed in an integrated framework into multi-label multi-instance data, including region and image labels. The framework determines local association data based on each region of an image. Other multi-label multi-instance data is based on relationships between region labels of the image, relationships between image labels of the image, and relationships between the region and image labels. These data are combined to classify the image. Training is also described.
摘要翻译：基于根据语义标签和区域的基于多标签多实例数据学习的分类，描述了图像被分类（例如，分组和/或标记）的技术。图像在集成框架中被处理成多标签多实例数据，包括区域和图像标签。该框架基于图像的每个区域确定局部关联数据。其他多标签多实例数据基于图像的区域标签之间的关系，图像的图像标签之间的关系以及区域和图像标签之间的关系。组合这些数据以对图像进行分类。培训也被描述。

16. 发明申请

US20120110432A1 Tool for Automated Online Blog Generation 审中-公开
标题翻译：自动在线博客生成工具
公开(公告)号：US20120110432A1
公开(公告)日：2012-05-03
申请号：US12916331
申请日：2010-10-29
申请人： Tao Mei , Xian-Sheng Hua , Shipeng Li
发明人： Tao Mei , Xian-Sheng Hua , Shipeng Li
IPC分类号： G06F17/00
CPC分类号： G06F17/248 , G06F17/241
摘要： Techniques for the design and operation of a blogging tool for automated blog creation and automated upload to a server are described herein. A content capturing process may obtain a plurality of images, including still images or video, as well as audio capture of voices and other sound, according to direction of a user operating an image-capture device. One or more of the images may be annotated with metadata or with text, which may be derived from verbal content provided by the user. A template may be selected in either an automated or user-controlled manner. The images and other content may be assembled into the template to form a blog entry. The blog entry may be uploaded to a server or otherwise shared. In one example, the uploading may be in response to a single user command, obtained by operation of a physical user interface or from verbal user input.
摘要翻译：本文描述了用于设计和操作用于自动化博客创建和自动上传到服务器的博客工具的技术。内容捕获处理可以根据操作图像捕获设备的用户的方向获得包括静止图像或视频的多个图像以及语音和其他声音的音频捕获。一个或多个图像可以用元数据或文本注释，其可以从用户提供的语言内容导出。可以以自动或用户控制的方式选择模板。图像和其他内容可以被组合到模板中以形成博客条目。博客条目可以上传到服务器或以其他方式共享。在一个示例中，上传可以响应于通过操作物理用户界面或从语言用户输入获得的单个用户命令。

17. 发明申请

US20110196859A1 Visual Search Reranking 有权
标题翻译：视觉搜索Reranking
公开(公告)号：US20110196859A1
公开(公告)日：2011-08-11
申请号：US12701283
申请日：2010-02-05
申请人： Tao Mei , Xian-Sheng Hua , Shipeng Li , Yuan Liu
发明人： Tao Mei , Xian-Sheng Hua , Shipeng Li , Yuan Liu
IPC分类号： G06F17/30
CPC分类号： G06F17/3053 , G06F17/30554
摘要： An initial ranked list of a first plurality of visual documents is obtained from a first source in response to a query, and a second plurality of visual documents relevant to the query is gathered from a plurality of second sources. Visual patterns identified from the second plurality of visual documents are compared with the first visual documents for reranking the first visual documents.
摘要翻译：响应于查询，从第一来源获得第一多个视觉文档的初始排列列表，并且从多个第二源收集与查询相关的第二多个视觉文档。将从第二多个视觉文档识别的视觉图案与用于重新排列第一视觉文档的第一视觉文档进行比较。

18. 发明授权

US07890512B2 Automatic image annotation using semantic distance learning 失效
标题翻译：使用语义远程学习的自动图像注释
公开(公告)号：US07890512B2
公开(公告)日：2011-02-15
申请号：US12136773
申请日：2008-06-11
申请人： Tao Mei , Xian-Sheng Hua , Shipeng Li , Yong Wang
发明人： Tao Mei , Xian-Sheng Hua , Shipeng Li , Yong Wang
IPC分类号： G06F17/30
CPC分类号： G06F17/3028 , G06F17/30265
摘要： Images are automatically annotated using semantic distance learning. Training images are manually annotated and partitioned into semantic clusters. Semantic distance functions (SDFs) are learned for the clusters. The SDF for each cluster is used to compute semantic distance scores between a new image and each image in the cluster. The scores for each cluster are used to generate a ranking list which ranks each image in the cluster according to its semantic distance from the new image. An association probability is estimated for each cluster which specifies the probability of the new image being semantically associated with the cluster. Cluster-specific probabilistic annotations for the new image are generated from the manual annotations for the images in each cluster. The association probabilities and cluster-specific probabilistic annotations for all the clusters are used to generate final annotations for the new image.
摘要翻译：图像使用语义远程学习自动注释。训练图像被手动注释并分割成语义聚类。为群集学习语义距离函数（SDF）。每个群集的SDF用于计算新图像和群集中每个图像之间的语义距离分数。每个群集的分数用于生成排序列表，根据与新图像的语义距离对群集中的每个图像进行排序。对于指定新图像与集群语义关联的概率的每个集群估计关联概率。针对新图像的集群特定概率注释是从每个集群中的图像的手动注释生成的。用于所有集群的关联概率和集群特定概率注释用于生成新图像的最终注释。

19. 发明申请

US20090313294A1 AUTOMATIC IMAGE ANNOTATION USING SEMANTIC DISTANCE LEARNING 失效
标题翻译：使用语义距离学习的自动图像注释
公开(公告)号：US20090313294A1
公开(公告)日：2009-12-17
申请号：US12136773
申请日：2008-06-11
申请人： Tao Mei , Xian-Sheng Hua , Shipeng Li , Yong Wang
发明人： Tao Mei , Xian-Sheng Hua , Shipeng Li , Yong Wang
IPC分类号： G06F17/30
CPC分类号： G06F17/3028 , G06F17/30265
摘要： Images are automatically annotated using semantic distance learning. Training images are manually annotated and partitioned into semantic clusters. Semantic distance functions (SDFs) are learned for the clusters. The SDF for each cluster is used to compute semantic distance scores between a new image and each image in the cluster. The scores for each cluster are used to generate a ranking list which ranks each image in the cluster according to its semantic distance from the new image. An association probability is estimated for each cluster which specifies the probability of the new image being semantically associated with the cluster. Cluster-specific probabilistic annotations for the new image are generated from the manual annotations for the images in each cluster. The association probabilities and cluster-specific probabilistic annotations for all the clusters are used to generate final annotations for the new image.
摘要翻译：图像使用语义远程学习自动注释。训练图像被手动注释并划分为语义聚类。为群集学习语义距离函数（SDF）。每个群集的SDF用于计算新图像和群集中每个图像之间的语义距离分数。每个群集的分数用于生成排序列表，根据与新图像的语义距离对群集中的每个图像进行排序。对于指定新图像与集群语义关联的概率的每个集群估计关联概率。针对新图像的集群特定概率注释是从每个集群中的图像的手动注释生成的。用于所有集群的关联概率和集群特定概率注释用于生成新图像的最终注释。

20. 发明申请

US20070101269A1 Capture-intention detection for video content analysis 有权
标题翻译：视频内容分析的捕获意图检测
公开(公告)号：US20070101269A1
公开(公告)日：2007-05-03
申请号：US11263081
申请日：2005-10-31
申请人： Xian-Sheng Hua , Shipeng Li , Tao Mei
发明人： Xian-Sheng Hua , Shipeng Li , Tao Mei
IPC分类号： H04N5/76 , G11B27/00
CPC分类号： G06F17/30811 , G06K9/00711 , G11B27/28 , H04N21/4147 , H04N21/4223 , H04N21/4334 , H04N21/44008 , H04N21/84 , H04N21/8456 , H04N21/854
摘要： Systems and methods are described for detecting capture-intention in order to analyze video content. In one implementation, a system decomposes video structure into sub-shots, extracts intention-oriented features from the sub-shots, delineates intention units via the extracted features, and classifies the intention units into intention categories via the extracted features. A video library can be organized via the categorized intention units.
摘要翻译：描述了用于检测捕获意图以分析视频内容的系统和方法。在一个实现中，系统将视频结构分解为子照片，从子拍摄中提取面向意图的特征，通过提取的特征描绘意图单元，并通过提取的特征将意图单元分类成意图类别。可以通过分类意图单位组织视频库。

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