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51. 发明申请

US20120155774A1 Statistical Approach to Large-scale Image Annotation 有权
标题翻译：大规模图像注释的统计方法
公开(公告)号：US20120155774A1
公开(公告)日：2012-06-21
申请号：US13406804
申请日：2012-02-28
申请人： Mingjing Li , Xiaoguang Rui
发明人： Mingjing Li , Xiaoguang Rui
IPC分类号： G06K9/64 , G06K9/46
CPC分类号： G06K9/00684 , G06K2209/27
摘要： Statistical approaches to large-scale image annotation are described. Generally, the annotation technique includes compiling visual features and textual information from a number of images, hashing the images visual features, and clustering the images based on their hash values. An example system builds statistical language models from the clustered images and annotates the image by applying one of the statistical language models.
摘要翻译：描述了大规模图像注释的统计方法。通常，注释技术包括从许多图像编译视觉特征和文本信息，对图像进行散列视觉特征，并且基于它们的散列值对图像进行聚类。示例系统从群集图像构建统计语言模型，并通过应用统计语言模型之一来注释图像。

52. 发明授权

US08001121B2 Training a ranking function using propagated document relevance 有权
标题翻译：使用传播的文档相关性来训练排名功能
公开(公告)号：US08001121B2
公开(公告)日：2011-08-16
申请号：US11364576
申请日：2006-02-27
申请人： Jue Wang , Mingjing Li , Wei-Ying Ma , Zhiwei Li
发明人： Jue Wang , Mingjing Li , Wei-Ying Ma , Zhiwei Li
IPC分类号： G06F17/30
CPC分类号： G06F17/30657 , G06F17/30864
摘要： A method and system for propagating the relevance of labeled documents to a query to unlabeled documents is provided. The propagation system provides training data that includes queries, documents labeled with their relevance to the queries, and unlabeled documents. The propagation system then calculates the similarity between pairs of documents in the training data. The propagation system then propagates the relevance of the labeled documents to similar, but unlabeled, documents. The propagation system may iteratively propagate labels of the documents until the labels converge on a solution. The training data with the propagated relevances can then be used to train a ranking function.
摘要翻译：提供了一种用于将标记的文档的相关性传播到未标记文档的查询的方法和系统。传播系统提供包括查询，标记为与查询相关的文档以及未标记的文档的培训数据。传播系统然后计算训练数据中文档对之间的相似度。传播系统然后将标记的文档的相关性传播到类似但未标记的文档。传播系统可以迭代地传播文档的标签，直到标签收敛在解决方案上。然后可以使用具有传播相关性的训练数据来训练排序功能。

53. 发明申请

US20090297050A1 Statistical Approach to Large-scale Image Annotation 有权
标题翻译：大规模图像注释的统计方法
公开(公告)号：US20090297050A1
公开(公告)日：2009-12-03
申请号：US12130943
申请日：2008-05-30
申请人： Mingjing Li , Xiaoguang Rui
发明人： Mingjing Li , Xiaoguang Rui
IPC分类号： G06K9/62
CPC分类号： G06K9/00684 , G06K2209/27
摘要： Statistical approaches to large-scale image annotation are described. Generally, the annotation technique includes compiling visual features and textual information from a number of images, hashing the images visual features, and clustering the images based on their hash values. An example system builds statistical language models from the clustered images and annotates the image by applying one of the statistical language models.
摘要翻译：描述了大规模图像注释的统计方法。通常，注释技术包括从许多图像编译视觉特征和文本信息，对图像进行散列视觉特征，并且基于它们的散列值对图像进行聚类。示例系统从群集图像构建统计语言模型，并通过应用统计语言模型之一来注释图像。

54. 发明授权

US07275029B1 System and method for joint optimization of language model performance and size 有权
标题翻译：联合优化语言模型性能和尺寸的系统和方法
公开(公告)号：US07275029B1
公开(公告)日：2007-09-25
申请号：US09607786
申请日：2000-06-30
申请人： Jianfeng Gao , Kai-Fu Lee , Mingjing Li , Hai-Feng Wang , Dong-Feng Cai , Lee-Feng Chien
发明人： Jianfeng Gao , Kai-Fu Lee , Mingjing Li , Hai-Feng Wang , Dong-Feng Cai , Lee-Feng Chien
IPC分类号： G06F17/27
CPC分类号： G06F17/2735 , G06F17/274 , G06F17/2818
摘要： A method for the joint optimization of language model performance and size is presented comprising developing a language model from a tuning set of information, segmenting at least a subset of a received textual corpus and calculating a perplexity value for each segment and refining the language model with one or more segments of the received corpus based, at least in part, on the calculated perplexity value for the one or more segments.
摘要翻译：提出了一种用于联合优化语言模型性能和大小的方法，包括从调整的信息集开发语言模型，分割所接收的文本语料库的至少一个子集，并计算每个分段的困惑度值，并用至少部分地基于所计算的一个或多个段的困惑度值，所接收的语料库的一个或多个段。

55. 发明申请

US20070203940A1 Propagating relevance from labeled documents to unlabeled documents 有权
标题翻译：从标签文档到未标记的文档传播相关性
公开(公告)号：US20070203940A1
公开(公告)日：2007-08-30
申请号：US11364807
申请日：2006-02-27
申请人： Jue Wang , Mingjing Li , Wei-Ying Ma , Zhiwei Li
发明人： Jue Wang , Mingjing Li , Wei-Ying Ma , Zhiwei Li
IPC分类号： G06F17/00
CPC分类号： G06F17/30864
摘要： A method and system for propagating the relevance of labeled documents to a query to unlabeled documents is provided. The propagation system provides training data that includes queries, documents labeled with their relevance to the queries, and unlabeled documents. The propagation system then calculates the similarity between pairs of documents in the training data. The propagation system then propagates the relevance of the labeled documents to similar, but unlabeled, documents. The propagation system may iteratively propagate labels of the documents until the labels converge on a solution. The training data with the propagated relevances can then be used to train a ranking function.
摘要翻译：提供了一种用于将标记的文档的相关性传播到未标记文档的查询的方法和系统。传播系统提供包括查询，标记为与查询相关的文档以及未标记的文档的培训数据。传播系统然后计算训练数据中文档对之间的相似度。传播系统然后将标记的文档的相关性传播到类似但未标记的文档。传播系统可以迭代地传播文档的标签，直到标签收敛在解决方案上。然后可以使用具有传播相关性的训练数据来训练排序功能。

56. 发明授权

US07254529B2 Method and apparatus for distribution-based language model adaptation 有权
标题翻译：基于分布式语言模型适应的方法和装置
公开(公告)号：US07254529B2
公开(公告)日：2007-08-07
申请号：US11225543
申请日：2005-09-13
申请人： Jianfeng Gao , Mingjing Li
发明人： Jianfeng Gao , Mingjing Li
IPC分类号： G06F17/27 , G06F17/28 , G10L15/00
CPC分类号： G06F17/2715 , G10L15/065 , G10L15/1815
摘要： A method and apparatus are provided for adapting a language model to a task-specific domain. Under the method and apparatus, the relative frequency of n-grams in a small training set (i.e. task-specific training data set) and the relative frequency of n-grams in a large training set (i.e. out-of-domain training data set) are used to weight a distribution count of n-grams in the large training set. The weighted distributions are then used to form a modified language model by identifying probabilities for n-grams from the weighted distributions.
摘要翻译：提供了一种用于使语言模型适应于任务特定领域的方法和装置。在该方法和装置下，小训练集中的n-gram的相对频率（即任务特定的训练数据集）和大训练集中的n-gram的相对频率（即，域外训练数据集）用于在大训练集中加权n-g的分布计数。然后通过从加权分布中识别n克的概率，将加权分布用于形成修改后的语言模型。

57. 发明授权

US08457400B2 Patch-based texture histogram coding for fast image similarity search 有权
标题翻译：基于贴图的纹理直方图编码，用于快速图像相似性搜索
公开(公告)号：US08457400B2
公开(公告)日：2013-06-04
申请号：US12147832
申请日：2008-06-27
申请人： Lei Wu , Mingjing Li
发明人： Lei Wu , Mingjing Li
IPC分类号： G06K9/00
CPC分类号： H04N21/835 , G06F17/30247 , G06K9/4642 , G06T7/44 , G06T7/97 , G06T2207/10008 , G06T2207/20021 , H04N21/44008 , H04N21/44236
摘要： Techniques for identifying copied images based on an original image are described. The identifying copied image is based on creating unique and identifiable features that in turn are used to generate multiple histograms. The histograms are generated by patches of the image, where the patches are created by equally dividing the image. The combined patch histograms are representative of the image.
摘要翻译：描述了基于原始图像识别复制图像的技术。识别复制的图像基于创建唯一且可识别的特征，其又被用于生成多个直方图。直方图由图像的补丁生成，其中通过均匀分割图像来创建补丁。组合的斑块直方图代表图像。

58. 发明授权

US08150170B2 Statistical approach to large-scale image annotation 有权
标题翻译：大规模图像注释的统计方法
公开(公告)号：US08150170B2
公开(公告)日：2012-04-03
申请号：US12130943
申请日：2008-05-30
申请人： Mingjing Li , Xiaoguang Rui
发明人： Mingjing Li , Xiaoguang Rui
IPC分类号： G06K9/72
CPC分类号： G06K9/00684 , G06K2209/27
摘要： Statistical approaches to large-scale image annotation are described. Generally, the annotation technique includes compiling visual features and textual information from a number of images, hashing the images visual features, and clustering the images based on their hash values. An example system builds statistical language models from the clustered images and annotates the image by applying one of the statistical language models.
摘要翻译：描述了大规模图像注释的统计方法。通常，注释技术包括从许多图像编译视觉特征和文本信息，对图像进行散列视觉特征，并且基于它们的散列值对图像进行聚类。示例系统从群集图像构建统计语言模型，并通过应用统计语言模型之一来注释图像。

59. 发明授权

US08019763B2 Propagating relevance from labeled documents to unlabeled documents 有权
标题翻译：从标签文档到未标记的文档传播相关性
公开(公告)号：US08019763B2
公开(公告)日：2011-09-13
申请号：US11364807
申请日：2006-02-27
申请人： Jue Wang , Mingjing Li , Wei-Ying Ma , Zhiwei Li
发明人： Jue Wang , Mingjing Li , Wei-Ying Ma , Zhiwei Li
IPC分类号： G07F17/30 , G07F7/00
CPC分类号： G06F17/30864
摘要： A method and system for propagating the relevance of labeled documents to a query to unlabeled documents is provided. The propagation system provides training data that includes queries, documents labeled with their relevance to the queries, and unlabeled documents. The propagation system then calculates the similarity between pairs of documents in the training data. The propagation system then propagates the relevance of the labeled documents to similar, but unlabeled, documents. The propagation system may iteratively propagate labels of the documents until the labels converge on a solution. The training data with the propagated relevances can then be used to train a ranking function.
摘要翻译：提供了一种用于将标记的文档的相关性传播到未标记文档的查询的方法和系统。传播系统提供包括查询，标记为与查询相关的文档以及未标记的文档的培训数据。传播系统然后计算训练数据中文档对之间的相似度。传播系统然后将标记的文档的相关性传播到类似但未标记的文档。传播系统可以迭代地传播文档的标签，直到标签收敛在解决方案上。然后可以使用具有传播相关性的训练数据来训练排序功能。

60. 发明授权

US07840502B2 Classification of images as advertisement images or non-advertisement images of web pages 有权
标题翻译：将图像分类为网页的广告图像或非广告图像
公开(公告)号：US07840502B2
公开(公告)日：2010-11-23
申请号：US11762553
申请日：2007-06-13
申请人： Mingjing Li , Zhiwei Li , Dongfang Li , Bin Wang
发明人： Mingjing Li , Zhiwei Li , Dongfang Li , Bin Wang
IPC分类号： G06F15/18
CPC分类号： G06Q30/02 , G06Q30/0277
摘要： An advertisement image classification system trains a binary classifier to classify images as advertisement images or non-advertisement images and then uses the binary classifier to classify images of web pages as advertisement images or non-advertisement images. During a training phase, the classification system generates training data of feature vectors representing the images and labels indicating whether an image is an advertisement image or a non-advertisement image. The classification system trains a binary classifier to classify images using training data. During a classification phase, the classification system inputs a web page with an image and generates a feature vector for the image. The classification system then applies the trained binary classifier to the feature vector to generate a score indicating whether the image is an advertisement image or a non-advertisement image.
摘要翻译：广告图像分类系统训练二进制分类器将图像分类为广告图像或非广告图像，然后使用二进制分类器将网页的图像分类为广告图像或非广告图像。在训练阶段，分类系统生成表示图像的特征向量的训练数据，以及指示图像是广告图像还是非广告图像的标签。分类系统训练二进制分类器，以使用训练数据对图像进行分类。在分类阶段，分类系统输入具有图像的网页，并生成图像的特征向量。然后，分类系统将经过训练的二进制分类器应用于特征向量，以生成指示图像是广告图像还是非广告图像的分数。

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