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1. 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。

2. 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。

3. 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。

4. 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。

5. 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。

6. 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。

7. 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。

8. 发明授权

US07809185B2 Extracting dominant colors from images using classification techniques 有权
标题翻译：使用分类技术从图像中提取主色
公开(公告)号：US07809185B2
公开(公告)日：2010-10-05
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
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。

9. 发明申请

US20090063455A1 Bipartite Graph Reinforcement Modeling to Annotate Web Images 有权
标题翻译：用于注释Web图像的二分图加固建模
公开(公告)号：US20090063455A1
公开(公告)日：2009-03-05
申请号：US11848157
申请日：2007-08-30
申请人： Mingjing Li , Wei-Ying Ma , Zhiwei Li , Xiaoguang Rui
发明人： Mingjing Li , Wei-Ying Ma , Zhiwei Li , Xiaoguang Rui
IPC分类号： G06F17/30
CPC分类号： G06F17/30265 , G06F17/30864
摘要： Systems and methods for bipartite graph reinforcement modeling to annotate web images are described. In one aspect the systems and methods implement bipartite graph reinforcement modeling operations to identify a set of annotations that are relevant to a Web image. The systems and methods annotate the Web image with the identified annotations. The systems and methods then index the annotated Web image. Responsive to receiving an image search query from a user, wherein the image search query comprises information relevant to at least a subset of the identified annotations, the image search engine service presents the annotated Web image to the user.
摘要翻译：描述了用于注释网络图像的二分图加强建模的系统和方法。在一个方面，系统和方法实现二分图加强建模操作，以识别与Web图像相关的一组注释。系统和方法用已识别的注释注释Web图像。系统和方法然后索引注释的Web图像。响应于从用户接收图像搜索查询，其中所述图像搜索查询包括与所识别的注释的至少一个子集相关的信息，所述图像搜索引擎服务将所述注释的Web图像呈现给所述用户。

10. 发明授权

US08321424B2 Bipartite graph reinforcement modeling to annotate web images 有权
标题翻译：双边图加强建模以注释网页图像
公开(公告)号：US08321424B2
公开(公告)日：2012-11-27
申请号：US11848157
申请日：2007-08-30
申请人： Mingjing Li , Wei-Ying Ma , Zhiwei Li , Xiaoguang Rui
发明人： Mingjing Li , Wei-Ying Ma , Zhiwei Li , Xiaoguang Rui
IPC分类号： G06F7/00 , G06F17/30
CPC分类号： G06F17/30265 , G06F17/30864
摘要： Systems and methods for bipartite graph reinforcement modeling to annotate web images are described. In one aspect the systems and methods implement bipartite graph reinforcement modeling operations to identify a set of annotations that are relevant to a Web image. The systems and methods annotate the Web image with the identified annotations. The systems and methods then index the annotated Web image. Responsive to receiving an image search query from a user, wherein the image search query comprises information relevant to at least a subset of the identified annotations, the image search engine service presents the annotated Web image to the user.
摘要翻译：描述了用于注释网络图像的二分图加强建模的系统和方法。在一个方面，系统和方法实现二分图加强建模操作，以识别与Web图像相关的一组注释。系统和方法用已识别的注释注释Web图像。系统和方法然后索引注释的Web图像。响应于从用户接收图像搜索查询，其中所述图像搜索查询包括与所识别的注释的至少一个子集相关的信息，所述图像搜索引擎服务将所述注释的Web图像呈现给所述用户。

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