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公开(公告)号：US20120301014A1
公开(公告)日：2012-11-29
申请号：US13118282
申请日：2011-05-27
CPC分类号： G06K9/4676 , G06F16/583
摘要： Tools and techniques for learning to rank local interest points from images using a data-driven scale-invariant feature transform (SIFT) approach termed “Rank-SIFT” are described herein. Rank-SIFT provides a flexible framework to select stable local interest points using supervised learning. A Rank-SIFT application detects interest points, learns differential features, and implements ranking model training in the Gaussian scale space (GSS). In various implementations a stability score is calculated for ranking the local interest points by extracting features from the GSS and characterizing the local interest points based on the features being extracted from the GSS across images containing the same visual objects.
摘要翻译： 本文描述了使用称为Rank-SIFT的数据驱动的尺度不变特征变换（SIFT）方法学习从图像对本地兴趣点进行排名的工具和技术。 Rank-SIFT提供了一个灵活的框架，使用监督学习选择稳定的本地兴趣点。 Rank-SIFT应用程序检测兴趣点，学习差异特征，并实现高斯尺度空间（GSS）中的排名模型训练。 在各种实施方式中，通过从GSS提取特征并基于从包含相同视觉对象的图像从GSS提取的特征来表征局部兴趣点来计算稳定性分数以对局部兴趣点进行排名。
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公开(公告)号：US20140368620A1
公开(公告)日：2014-12-18
申请号：US13919933
申请日：2013-06-17
CPC分类号： H04N13/282 , G06F3/00 , G06T19/006 , H04N5/23216 , H04N5/23293 , H04N13/221 , H04N13/296
摘要： A method of acquiring a set of images useable to 3D model a physical object includes imaging the physical object with a camera, and displaying with the camera a current view of the physical object as imaged by the camera from a current perspective. The method further includes displaying with the camera a visual cue overlaying the current view and indicating perspectives from which the physical object is to be imaged to acquire the set of images.
摘要翻译： 获取可用于对物理对象进行3D建模的一组图像的方法包括用相机对物理对象进行成像，并且从当前的角度通过相机显示物理对象的当前视图。 该方法还包括用相机显示覆盖当前视图的视觉提示，并且指示要从其中成像物理对象的视角以获取该组图像。
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公开(公告)号：US08326820B2
公开(公告)日：2012-12-04
申请号：US12571302
申请日：2009-09-30
申请人： Zhiwei Li , Lei Zhang , Rui Cai , Wei-Ying Ma , Heung-Yeung Shum
发明人： Zhiwei Li , Lei Zhang , Rui Cai , Wei-Ying Ma , Heung-Yeung Shum
IPC分类号： G06F17/30
CPC分类号： G06F17/3028 , G06F17/30448
摘要： Described herein is a technology that facilitates efficient large-scale similarity-based retrieval. In several embodiments documents, images, and/or other multimedia files are compactly represented and efficiently indexed to enable robust search using a long-query in a large-scale corpus. As described herein, these techniques include performing decomposition of a file, e.g., a document or document-like representation. The techniques use dimension reduction to obtain three parts, topic-related words (major semantics), document specific words (minor semantics), and background words, representing the major semantics in a feature vector and the minor semantics as keywords. Using the techniques described, file vectors are matched in a topic model and the results ranked based on the keywords.
摘要翻译： 这里描述了一种有助于有效的大规模相似性检索的技术。 在几个实施例中，文档，图像和/或其他多媒体文件被紧凑地表示并且被有效地索引，以使得能够使用大规模语料库中的长查询进行鲁棒搜索。 如这里所述，这些技术包括执行文件的分解，例如文档或类似文档的表示。 这些技术使用维度缩减来获得三个部分，主题相关词（主要语义），文档特定词（次要语义）和背景词，表示特征向量中的主要语义和次要语义作为关键字。 使用所描述的技术，在主题模型中匹配文件向量，并根据关键字对结果进行排名。
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公开(公告)号：US08954425B2
公开(公告)日：2015-02-10
申请号：US12796345
申请日：2010-06-08
申请人： Rong Xiao , Qiang Hao , Changhu Wang , Rui Cai , Lei Zhang
发明人： Rong Xiao , Qiang Hao , Changhu Wang , Rui Cai , Lei Zhang
IPC分类号： G06F17/30
CPC分类号： G06F17/30867 , G06F17/30241
摘要： Described herein is a technology that facilitates efficient automated mining of topic-related aspects of user-generated content based on automated analysis of the user-generated content. Locations are automatically learned based on dividing documents into document segments, and decomposing the segments into local topics and global topics. Techniques are described that facilitate automatically extracting snippets. These techniques include, for example, computer annotating travelogues with learned tags and images, performing topic learning to obtain an interest model, performing location matching based on the interest model, calculating geographic and semantic relevance scores, ranking snippets based on the geographic and semantic relevance scores, and searching snippets with a “location+context term” query.
摘要翻译： 这里描述了一种技术，其有助于基于对用户生成的内容的自动化分析来有效地自动挖掘用户生成的内容的主题相关方面。 根据将文档分割成文档段，自动学习位置，并将段分解为本地主题和全局主题。 描述了便于自动提取代码段的技术。 这些技术包括例如计算机注释具有学习标签和图像的旅行记录，执行主题学习以获得兴趣模型，基于兴趣模型执行位置匹配，计算地理和语义相关性分数，基于地理和语义相关性来排序片段 分数和搜索带有“位置+上下文术语”查询的片段。
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公开(公告)号：US20110302162A1
公开(公告)日：2011-12-08
申请号：US12796345
申请日：2010-06-08
申请人： Rong Xiao , Qiang Hao , Changhu Wang , Rui Cai , Lei Zhang
发明人： Rong Xiao , Qiang Hao , Changhu Wang , Rui Cai , Lei Zhang
IPC分类号： G06F17/30
CPC分类号： G06F17/30867 , G06F17/30241
摘要： Described herein is a technology that facilitates efficient automated mining of topic-related aspects of user-generated content based on automated analysis of the user-generated content. Locations are automatically learned based on dividing documents into document segments, and decomposing the segments into local topics and global topics. Techniques are described that facilitate automatically extracting snippets. These techniques include, for example, computer annotating travelogues with learned tags and images, performing topic learning to obtain an interest model, performing location matching based on the interest model, calculating geographic and semantic relevance scores, ranking snippets based on the geographic and semantic relevance scores, and searching snippets with a “location+context term” query.
摘要翻译： 这里描述了一种技术，其有助于基于对用户生成的内容的自动化分析来有效地自动挖掘用户生成的内容的主题相关方面。 根据将文档分割成文档段，自动学习位置，并将段分解为本地主题和全局主题。 描述了便于自动提取代码段的技术。 这些技术包括例如计算机注释具有学习标签和图像的旅行记录，执行主题学习以获得兴趣模型，基于兴趣模型执行位置匹配，计算地理和语义相关性分数，基于地理和语义相关性来排序片段 分数和搜索带有“位置+上下文术语”查询的片段。
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公开(公告)号：US09082040B2
公开(公告)日：2015-07-14
申请号：US13107717
申请日：2011-05-13
申请人： Rui Cai , Zhiwei Li , Lei Zhang , Wenbin Tang
发明人： Rui Cai , Zhiwei Li , Lei Zhang , Wenbin Tang
CPC分类号： G06K9/4671 , G06F17/30256 , G06K9/4676
摘要： Tools and techniques for identifying visual contextual synonyms are described herein. The described operations use visual words having similar contextual distributions as contextual synonyms to identify and describe visual objects that share semantic meaning. The contextual distribution of a visual word is described using the statistics of co-occurrence and spatial information averaged over image patches that share the visual word. In various implementations, the techniques are employed to construct a visual contextual synonym dictionary for a large visual vocabulary. In various implementations, the visual contextual synonym dictionary narrows the semantic gap for large-scale visual search.
摘要翻译： 本文描述了用于识别视觉上下文同义词的工具和技术。 所描述的操作使用具有相似语境分布的视觉词作为上下文同义词来识别和描述共享语义意义的视觉对象。 使用共享视觉词的图像补丁上平均的同现和空间信息的统计来描述视觉词的语境分布。 在各种实现中，使用这些技术来构建用于大型视觉词汇表的视觉上下文同义词字典。 在各种实现中，视觉上下文同义词词典缩小了大规模视觉搜索的语义差距。
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公开(公告)号：US08983201B2
公开(公告)日：2015-03-17
申请号：US13561718
申请日：2012-07-30
CPC分类号： G06K9/46 , G06K9/4676 , G06K9/469
摘要： The techniques discussed herein discover three-dimensional (3-D) visual phrases for an object based on a 3-D model of the object. The techniques then describe the 3-D visual phrases. Once described, the techniques use the 3-D visual phrases to detect the object in an image (e.g., object recognition).
摘要翻译： 本文讨论的技术基于对象的3-D模型发现对象的三维（3-D）视觉短语。 然后，技术描述3-D视觉短语。 一旦描述，这些技术使用3-D视觉短语来检测图像中的对象（例如，对象识别）。
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公开(公告)号：US20120290577A1
公开(公告)日：2012-11-15
申请号：US13107717
申请日：2011-05-13
申请人： Rui Cai , Zhiwei Li , Lei Zhang , Wenbin Tang
发明人： Rui Cai , Zhiwei Li , Lei Zhang , Wenbin Tang
IPC分类号： G06F17/30
CPC分类号： G06K9/4671 , G06F17/30256 , G06K9/4676
摘要： Tools and techniques for identifying visual contextual synonyms are described herein. The described operations use visual words having similar contextual distributions as contextual synonyms to identify and describe visual objects that share semantic meaning. The contextual distribution of a visual word is described using the statistics of co-occurrence and spatial information averaged over image patches that share the visual word. In various implementations, the techniques are employed to construct a visual contextual synonym dictionary for a large visual vocabulary. In various implementations, the visual contextual synonym dictionary narrows the semantic gap for large-scale visual search.
摘要翻译： 本文描述了用于识别视觉上下文同义词的工具和技术。 所描述的操作使用具有相似语境分布的视觉词作为上下文同义词来识别和描述共享语义意义的视觉对象。 使用共享视觉词的图像补丁上平均的同现和空间信息的统计来描述视觉词的语境分布。 在各种实现中，使用这些技术来构建用于大型视觉词汇表的视觉上下文同义词字典。 在各种实现中，视觉上下文同义词词典缩小了大规模视觉搜索的语义差距。
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公开(公告)号：US20110078159A1
公开(公告)日：2011-03-31
申请号：US12571302
申请日：2009-09-30
申请人： Zhiwei Li , Lei Zhang , Rui Cai , Wei-Ying Ma , Heung-Yeung Shum
发明人： Zhiwei Li , Lei Zhang , Rui Cai , Wei-Ying Ma , Heung-Yeung Shum
IPC分类号： G06F17/30
CPC分类号： G06F17/3028 , G06F17/30448
摘要： Described herein is a technology that facilitates efficient large-scale similarity-based retrieval. In several embodiments documents, images, and/or other multimedia files are compactly represented and efficiently indexed to enable robust search using a long-query in a large-scale corpus. As described herein, these techniques include performing decomposition of a file, e.g., a document or document-like representation. The techniques use dimension reduction to obtain three parts, topic-related words (major semantics), document specific words (minor semantics), and background words, representing the major semantics in a feature vector and the minor semantics as keywords. Using the techniques described, file vectors are matched in a topic model and the results ranked based on the keywords.
摘要翻译： 这里描述了一种有助于有效的大规模相似性检索的技术。 在几个实施例中，文档，图像和/或其他多媒体文件被紧凑地表示并且被有效地索引，以使得能够使用大规模语料库中的长查询进行鲁棒搜索。 如这里所述，这些技术包括执行文件的分解，例如文档或类似文档的表示。 这些技术使用维度缩减来获得三个部分，主题相关词（主要语义），文档特定词（次要语义）和背景词，表示特征向量中的主要语义和次要语义作为关键字。 使用所描述的技术，在主题模型中匹配文件向量，并根据关键字对结果进行排名。
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公开(公告)号：US20140029856A1
公开(公告)日：2014-01-30
申请号：US13561718
申请日：2012-07-30
IPC分类号： G06K9/46
CPC分类号： G06K9/46 , G06K9/4676 , G06K9/469
摘要： The techniques discussed herein discover three-dimensional (3-D) visual phrases for an object based on a 3-D model of the object. The techniques then describe the 3-D visual phrases. Once described, the techniques use the 3-D visual phrases to detect the object in an image (e.g., object recognition).
摘要翻译： 本文讨论的技术基于对象的3-D模型发现对象的三维（3-D）视觉短语。 然后，技术描述3-D视觉短语。 一旦描述，这些技术使用3-D视觉短语来检测图像中的对象（例如，对象识别）。
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