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1. 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。

2. 发明申请

US20110058734A1 CLASSIFICATION OF IMAGES AS ADVERTISEMENT IMAGES OR NON-ADVERTISEMENT IMAGES 有权
标题翻译：图像分类作为广告图像或非广告图像
公开(公告)号：US20110058734A1
公开(公告)日：2011-03-10
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： G06K9/62
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。

3. 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。

4. 发明申请

US20080313031A1 CLASSIFICATION OF IMAGES AS ADVERTISEMENT IMAGES OR NON-ADVERTISEMENT IMAGES 有权
标题翻译：图像分类作为广告图像或非广告图像
公开(公告)号：US20080313031A1
公开(公告)日：2008-12-18
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： G06Q30/00
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。

5. 发明授权

US07647331B2 Detecting duplicate images using hash code grouping 有权
标题翻译：使用哈希码分组检测重复的图像
公开(公告)号：US07647331B2
公开(公告)日：2010-01-12
申请号：US11277727
申请日：2006-03-28
申请人： Mingjing Li , Bin Wang , Wei-Ying Ma , Zhiwei Li
发明人： Mingjing Li , Bin Wang , Wei-Ying Ma , Zhiwei Li
IPC分类号： G06F7/00 , G06F17/00 , G06K9/56 , G06K9/68
CPC分类号： G06F17/30864
摘要： A duplicate image detection system generates an image table that maps hash codes of images to their corresponding images. The image table may group images according to their group identifiers generated from the most significant elements of the hash codes based on significance of the elements in representing an image. The image table thus segregates images by their group identifiers. To detect a duplicate image of a target image, the detection system generates a target hash code for the target image. The detection system then identifies the group of the target image based on the group identifier of the target hash code. After identifying the group identifier, the detection system searches the corresponding group table to identify hash codes that have values that are similar to the target hash code. The detection system then selects the images associated with those similar hash codes as being duplicates of the target image.
摘要翻译：复制图像检测系统生成将图像的哈希码映射到其对应图像的图像表。图像表可以根据基于代表图像的元素的重要性从哈希码的最重要元素生成的组标识符来对图像进行分组。因此，图像表通过其组标识符隔离图像。为了检测目标图像的重复图像，检测系统生成目标图像的目标散列码。然后，检测系统基于目标散列码的组标识符来识别目标图像的组。在识别组标识符之后，检测系统搜索对应的组表以识别具有与目标散列码相似的值的散列码。然后，检测系统选择与这些类似的哈希码相关联的图像作为目标图像的重复。

6. 发明申请

US20090076800A1 Dual Cross-Media Relevance Model for Image Annotation 有权
标题翻译：图像注释的双重跨媒体相关性模型
公开(公告)号：US20090076800A1
公开(公告)日：2009-03-19
申请号：US11956331
申请日：2007-12-13
申请人： Mingjing Li , Jing Liu , Bin Wang , Zhiwei Li , Wei-Ying Ma
发明人： Mingjing Li , Jing Liu , Bin Wang , Zhiwei Li , Wei-Ying Ma
IPC分类号： G06F17/21
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。

7. 发明申请

US20070239756A1 Detecting Duplicate Images Using Hash Code Grouping 有权
标题翻译：使用哈希代码分组检测重复的图像
公开(公告)号：US20070239756A1
公开(公告)日：2007-10-11
申请号：US11277727
申请日：2006-03-28
申请人： Mingjing Li , Bin Wang , Wei-Ying Ma , Zhiwei Li
发明人： Mingjing Li , Bin Wang , Wei-Ying Ma , Zhiwei Li
IPC分类号： G06F7/00
CPC分类号： G06F17/30864
摘要： A duplicate image detection system generates an image table that maps hash codes of images to their corresponding images. The image table may group images according to their group identifiers generated from the most significant elements of the hash codes based on significance of the elements in representing an image. The image table thus segregates images by their group identifiers. To detect a duplicate image of a target image, the detection system generates a target hash code for the target image. The detection system then identifies the group of the target image based on the group identifier of the target hash code. After identifying the group identifier, the detection system searches the corresponding group table to identify hash codes that have values that are similar to the target hash code. The detection system then selects the images associated with those similar hash codes as being duplicates of the target image.
摘要翻译：复制图像检测系统生成将图像的哈希码映射到其对应图像的图像表。图像表可以根据基于代表图像的元素的重要性从哈希码的最重要元素生成的组标识符来对图像进行分组。因此，图像表通过其组标识符隔离图像。为了检测目标图像的重复图像，检测系统生成目标图像的目标散列码。然后，检测系统基于目标散列码的组标识符来识别目标图像的组。在识别组标识符之后，检测系统搜索对应的组表以识别具有与目标散列码相似的值的散列码。然后，检测系统选择与这些类似的哈希码相关联的图像作为目标图像的重复。

8. 发明授权

US08457416B2 Estimating word correlations from images 有权
标题翻译：从图像估计字相关性
公开(公告)号：US08457416B2
公开(公告)日：2013-06-04
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。

9. 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。

10. 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。

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