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41. 发明申请

US20090074306A1 Estimating Word Correlations from Images 有权
标题翻译：估计图像中的词相关性
公开(公告)号：US20090074306A1
公开(公告)日：2009-03-19
申请号：US11956333
申请日：2007-12-13
申请人： Jing Liu , Bin Wang , Zhiwei Li , Mingjing Li , Wei-Ying Ma
发明人： Jing Liu , Bin Wang , Zhiwei Li , Mingjing Li , Wei-Ying Ma
IPC分类号： G06K9/72
CPC分类号： G06F17/30247 , G06F17/30731
摘要： Word correlations are estimated using a content-based method, which uses visual features of image representations of the words. The image representations of the subject words may be generated by retrieving images from data sources (such as the Internet) using image search with the subject words as query words. One aspect of the techniques is based on calculating the visual distance or visual similarity between the sets of retrieved images corresponding to each query word. The other is based on calculating the visual consistence among the set of the retrieved images corresponding to a conjunctive query word. The combination of the content-based method and a text-based method may produce even better result.
摘要翻译：使用基于内容的方法来估计词相关性，其使用词的图像表示的视觉特征。可以通过使用将主题词作为查询词的图像搜索从数据源（例如因特网）检索图像来生成主题词的图像表示。该技术的一个方面是基于计算对应于每个查询词的检索图像组之间的视觉距离或视觉相似度。另一个是基于计算与连接查询词对应的检索到的图像的集合之间的视觉一致性。基于内容的方法和基于文本的方法的组合可以产生更好的结果。

42. 发明申请

US20070196013A1 Automatic classification of photographs and graphics 有权
标题翻译：自动分类照片和图形
公开(公告)号：US20070196013A1
公开(公告)日：2007-08-23
申请号：US11358705
申请日：2006-02-21
申请人： Mingjing Li , Wei-Ying Ma , Yuanhao Chen , Zhiwei Li
发明人： Mingjing Li , Wei-Ying Ma , Yuanhao Chen , Zhiwei Li
IPC分类号： G06K9/62 , G06K9/00
CPC分类号： G06K9/00456
摘要： A method and system for classifying an image as a photograph or a graphic based on a ranked prevalent color histogram feature or a ranked region size feature is provided. The prevalent color histogram feature contains counts of the colors that are most prevalent in the image sorted in descending order. The region size feature contains counts of the largest regions of the image sorted in descending order. The classification system then classifies the image based on the ranked prevalent color histogram feature and/or the ranked region size feature using a previously trained classifier.
摘要翻译：提供了一种基于排序流行的颜色直方图特征或排名区域大小特征将图像分类为照片或图形的方法和系统。流行的颜色直方图特征包含按照降序排列的图像中最流行的颜色的计数。区域大小特征包含以降序排序的图像的最大区域的计数。然后，分类系统使用先前训练的分类器，基于排名普遍的颜色直方图特征和/或排名区域大小特征对图像进行分类。

43. 发明授权

US08571850B2 Dual cross-media relevance model for image annotation 有权
标题翻译：用于图像注释的双跨媒体相关性模型
公开(公告)号：US08571850B2
公开(公告)日：2013-10-29
申请号：US11956331
申请日：2007-12-13
申请人： Mingjing Li , Jing Lui , Bin Wang , Zhiwei Li , Wei-Ying Ma
发明人： Mingjing Li , Jing Lui , Bin Wang , Zhiwei Li , Wei-Ying Ma
IPC分类号： G06F17/27
CPC分类号： G06F17/241 , G06F17/2735
摘要： A dual cross-media relevance model (DCMRM) is used for automatic image annotation. In contrast to the traditional relevance models which calculate the joint probability of words and images over a training image database, the DCMRM model estimates the joint probability by calculating the expectation over words in a predefined lexicon. The DCMRM model may be advantageous because a predefined lexicon potentially has better behavior than a training image database. The DCMRM model also takes advantage of content-based techniques and image search techniques to define the word-to-image and word-to-word relations involved in image annotation. Both relations can be estimated by using image search techniques on the web data as well as available training data.
摘要翻译：双重跨媒体相关性模型（DCMRM）用于自动图像注释。与在训练图像数据库中计算单词和图像的联合概率的传统相关性模型相反，DCMRM模型通过计算预定义词典中的单词的期望来估计联合概率。 DCMRM模型可能是有利的，因为预定义词典潜在地具有比训练图像数据库更好的行为。 DCMRM模型还利用基于内容的技术和图像搜索技术来定义图像注释中涉及的单词到图像和单词对字的关系。可以通过使用图像搜索技术对网络数据以及可用的训练数据来估计这两个关系。

44. 发明授权

US08126274B2 Visual language modeling for image classification 有权
标题翻译：图像分类的视觉语言建模
公开(公告)号：US08126274B2
公开(公告)日：2012-02-28
申请号：US11847959
申请日：2007-08-30
申请人： Mingjing Li , Wei-Ying Ma , Zhiwei Li , Lei Wu
发明人： Mingjing Li , Wei-Ying Ma , Zhiwei Li , Lei Wu
IPC分类号： G06K9/62
CPC分类号： G06K9/4685 , G06K9/4642 , G06K9/6278
摘要： Systems and methods for visual language modeling for image classification are described. In one aspect the systems and methods model training images corresponding to multiple image categories as matrices of visual words. Visual language models are generated from the matrices. In view of a given image, for example, provided by a user or from the Web, the systems and methods determine an image category corresponding to the given image. This image categorization is accomplished by maximizing the posterior probability of visual words associated with the given image over the visual language models. The image category, or a result corresponding to the image category, is presented to the user.
摘要翻译：描述了用于图像分类的视觉语言建模的系统和方法。在一个方面，系统和方法将对应于多个图像类别的训练图像建模为视觉词的矩阵。视觉语言模型是从矩阵生成的。考虑到例如由用户或从Web提供的给定图像，系统和方法确定对应于给定图像的图像类别。这种图像分类是通过在视觉语言模型上最大化与给定图像相关联的视觉词的后验概率来实现的。图像类别或与图像类别对应的结果被呈现给用户。

45. 发明授权

US08027940B2 Classification of images as advertisement images or non-advertisement images 有权
标题翻译：图像分类为广告图像或非广告图像
公开(公告)号：US08027940B2
公开(公告)日：2011-09-27
申请号：US12945635
申请日：2010-11-12
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。

46. 发明授权

US07788263B2 Probabilistic retrospective event detection 有权
标题翻译：概率回顾事件检测
公开(公告)号：US07788263B2
公开(公告)日：2010-08-31
申请号：US11256353
申请日：2005-10-21
申请人： Zhiwei Li , Mingjing Li , Wei-Ying Ma
发明人： Zhiwei Li , Mingjing Li , Wei-Ying Ma
IPC分类号： G06F17/30 , G06F17/00
CPC分类号： G06F17/30684 , G06F17/30687
摘要： Probabilistic retrospective event detection is described. In one aspect, event parameters are initialized to identify a number of events from a corpus of documents. Using a generative model, documents are determined to be associated with an event to detect representative events from the identified number of events.
摘要翻译：描述概率回顾事件检测。在一个方面，事件参数被初始化以从文档语料库中识别多个事件。使用生成模型，文档被确定为与事件相关联以从所识别的事件数量检测代表事件。

47. 发明授权

US07657089B2 Automatic classification of photographs and graphics 有权
标题翻译：自动分类照片和图形
公开(公告)号：US07657089B2
公开(公告)日：2010-02-02
申请号：US11358705
申请日：2006-02-21
申请人： Mingjing Li , Wei-Ying Ma , Yuanhao Chen , Zhiwei Li
发明人： Mingjing Li , Wei-Ying Ma , Yuanhao Chen , Zhiwei Li
IPC分类号： G06K9/00
CPC分类号： G06K9/00456
摘要： A method and system for classifying an image as a photograph or a graphic based on a ranked prevalent color histogram feature or a ranked region size feature is provided. The prevalent color histogram feature contains counts of the colors that are most prevalent in the image sorted in descending order. The region size feature contains counts of the largest regions of the image sorted in descending order. The classification system then classifies the image based on the ranked prevalent color histogram feature and/or the ranked region size feature using a previously trained classifier.
摘要翻译：提供了一种基于排序流行的颜色直方图特征或排名区域大小特征将图像分类为照片或图形的方法和系统。流行的颜色直方图特征包含按照降序排列的图像中最流行的颜色的计数。区域大小特征包含以降序排序的图像的最大区域的计数。然后，分类系统使用先前训练的分类器，基于排名普遍的颜色直方图特征和/或排名区域大小特征对图像进行分类。

48. 发明申请

US20080075360A1 EXTRACTING DOMINANT COLORS FROM IMAGES USING CLASSIFICATION TECHNIQUES 有权
标题翻译：使用分类技术从图像提取主色
公开(公告)号：US20080075360A1
公开(公告)日：2008-03-27
申请号：US11533953
申请日：2006-09-21
申请人： Mingjing Li , Wei-Ying Ma , Zhiwei Li , Yuanhao Chen
发明人： Mingjing Li , Wei-Ying Ma , Zhiwei Li , Yuanhao Chen
IPC分类号： G06K9/62 , G06K9/46 , G06K9/00 , G06K9/66
CPC分类号： G06K9/4652 , G06T7/90 , G06T2207/10024 , G06T2207/20081
摘要： A method and system for generating a detector to detect a dominant color of an image is provided. A dominant color system trains a detector to classify colors as being dominant colors of images. The dominant color system trains the detector using a collection of training images. To train the detector, the dominant color system first identifies candidate dominant colors of the training images. The dominant color system then extracts features of the candidate dominant colors. The dominant color system also inputs an indication of dominance of each of the candidate dominant colors. The dominant color system then trains a detector to detect the dominant color of images using the extracted features and indications of dominance of the candidate dominant colors as training data.
摘要翻译：提供了一种用于生成用于检测图像的主要颜色的检测器的方法和系统。主色系统训练检测器将颜色分类为图像的主要颜色。主要颜色系统使用训练图像的集合训练检测器。为了训练检测器，主要颜色系统首先识别训练图像的候选主色。主要颜色系统然后提取候选主色的特征。主要颜色系统还输入每种候选主色的优势指示。主要颜色系统然后训练检测器以使用提取的特征和候选主色优势的指示作为训练数据来检测图像的主要颜色。

49. 发明申请

US20070203908A1 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：US20070203908A1
公开(公告)日：2007-08-30
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： G06F7/00
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。

50. 发明授权

US07124080B2 Method and apparatus for adapting a class entity dictionary used with language models 有权
公开(公告)号：US07124080B2
公开(公告)日：2006-10-17
申请号：US10008432
申请日：2001-11-13
申请人： Zheng Chen , Jianfeng Gao , Mingjing Li , Feng Zhang
发明人： Zheng Chen , Jianfeng Gao , Mingjing Li , Feng Zhang
IPC分类号： G10L15/06 , G10L15/00
CPC分类号： G06F17/2715 , G06F17/2775
摘要： A method and apparatus are provided for augmenting a language model with a class entity dictionary based on corrections made by a user. Under the method and apparatus, a user corrects an output that is based in part on the language model by replacing an output segment with a correct segment. The correct segment is added to a class of segments in the class entity dictionary and a probability of the correct segment given the class is estimated based on an n-gram probability associated with the output segment and an n-gram probability associated with the class. This estimated probability is then used to generate further outputs.

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