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31. 发明授权

US08543521B2 Supervised re-ranking for visual search 有权
标题翻译：视觉搜索的监督重新排名
公开(公告)号：US08543521B2
公开(公告)日：2013-09-24
申请号：US13076350
申请日：2011-03-30
申请人： Linjun Yang , Xian-Sheng Hua
发明人： Linjun Yang , Xian-Sheng Hua
IPC分类号： G06F15/18
CPC分类号： G06F17/30274 , G06F17/30247 , G06F17/30268
摘要： Supervised re-ranking for visual search may include re-ordering images that are returned in response to a text-based image search by exploiting visual information included in the images. In one example, supervised re-ranking for visual search may include receiving a textual query, obtaining an initial ranking result including a plurality of images corresponding to the textual query, and representing the textual query by a visual context of the plurality of images. A query-independent re-ranking model may be trained based on visual re-ranking features of the plurality of images of the textual query in accordance with a supervised training algorithm.
摘要翻译：视觉搜索的监督重新排序可以包括通过利用包括在图像中的视觉信息来重新排序响应于基于文本的图像搜索返回的图像。在一个示例中，用于视觉搜索的监督重新排序可以包括接收文本查询，获得包括对应于文本查询的多个图像的初始排名结果，以及通过多个图像的视觉上下文来表示文本查询。可以根据监督训练算法，基于文本查询的多个图像的视觉重新排列特征来训练不依赖于查询的重排序模型。

32. 发明授权

US08346767B2 Image search result summarization with informative priors 有权
标题翻译：图像搜索结果汇总与信息先验
公开(公告)号：US08346767B2
公开(公告)日：2013-01-01
申请号：US12764917
申请日：2010-04-21
申请人： Linjun Yang , Rui Liu , Xian-Sheng Hua
发明人： Linjun Yang , Rui Liu , Xian-Sheng Hua
IPC分类号： G06F17/30 , G06F7/00
CPC分类号： G06F17/3028
摘要： An informative priors image search result summarization system and method that summarizes image search results based on the image relevance (as determined by a search engine's initial ranking) and the image quality. Embodiments of the system and method cluster the image search results, rank images within each cluster based on a computed image score, and then select a summary image for the cluster. Each cluster is analyzed and an image in the cluster having the maximum image score is included in a selected summary collection. The image score is computed using the image relevance and the image quality, as well as a cluster coherence, a density, and a diversity. The selection of images from a collection of candidate images generates an image search result summarization, which is presented to a user. The summaries are presented to the user in a ranked order based on their image scores.
摘要翻译：一种信息先验图像搜索结果汇总系统和方法，其基于图像相关性（由搜索引擎的初始排名确定）和图像质量来总结图像搜索结果。系统和方法的实施例对图像搜索结果进行聚类，基于计算的图像分数对每个聚类内的图像进行排序，然后选择聚类的摘要图像。分析每个群集，并且具有最大图像得分的群集中的图像被包括在所选择的摘要集合中。使用图像相关性和图像质量以及簇相干性，密度和多样性来计算图像分数。来自候选图像集合的图像的选择产生呈现给用户的图像搜索结果汇总。这些摘要根据他们的图像分数以排序顺序呈现给用户。

33. 发明申请

US20120294477A1 Searching for Images by Video 有权
标题翻译：通过视频搜索图像
公开(公告)号：US20120294477A1
公开(公告)日：2012-11-22
申请号：US13110708
申请日：2011-05-18
申请人： Linjun Yang , Xian-Sheng Hua , Yang Cai
发明人： Linjun Yang , Xian-Sheng Hua , Yang Cai
IPC分类号： G06K9/46 , G06F17/30
CPC分类号： G06K9/4676 , G06F17/30796 , G06F17/30799 , G06K9/00671 , G06K9/00744 , G06K9/38 , G06K9/4642 , G06K9/4671 , G06K9/623
摘要： Techniques describe submitting a video clip as a query by a user. A process retrieves images and information associated with the images in response to the query. The process decomposes the video clip into a sequence of frames to extract the features in a frame and to quantize the extracted features into descriptive words. The process further tracks the extracted features as points in the frame, a first set of points to correspond to a second set of points in consecutive frames to construct a sequence of points. Then the process identifies the points that satisfy criteria of being stable points and being centrally located in the frame to represent the video clip as a bag of descriptive words for searching for images and information related to the video clip.
摘要翻译：技术描述提交视频剪辑作为用户的查询。响应于查询，进程检索与图像相关联的图像和信息。该过程将视频剪辑分解成帧序列以提取帧中的特征并将提取的特征量化为描述性词。该过程进一步跟踪提取的特征作为帧中的点，第一组点对应于连续帧中的第二组点以构成点序列。然后，该过程识别满足稳定点的标准并且位于帧中心的点以将视频剪辑表示为用于搜索与视频剪辑相关的图像和信息的描述词的一袋。

34. 发明授权

US08180766B2 Bayesian video search reranking 有权
标题翻译：贝叶斯视频搜索重新排名
公开(公告)号：US08180766B2
公开(公告)日：2012-05-15
申请号：US12235509
申请日：2008-09-22
申请人： Linjun Yang , Jingdong Wang , Xian-Sheng Hua , Xinmei Tian
发明人： Linjun Yang , Jingdong Wang , Xian-Sheng Hua , Xinmei Tian
IPC分类号： G06F17/30 , G06F7/00
CPC分类号： G06F17/30802 , G06F17/30817 , G06K9/6256 , G06K9/6259 , G06K9/6296
摘要： A general framework for video search reranking is disclosed which explicitly formulates reranking into a global optimization problem from the Bayesian perspective. Under this framework, with two novel pair-wise ranking distances, two effective video search reranking methods, hinge reranking and preference strength reranking, are disclosed. Experiments conducted on the TRECVID dataset have demonstrated that the disclosed methods outperform several existing reranking approaches.
摘要翻译：披露了视频搜索重新排列的一般框架，从贝叶斯角度明确地将重新排列到全局优化问题中。在这个框架下，通过两个新的成对排名距离，公开了两个有效的视频搜索重新排序方法，铰链重新排列和优先权重新排序。在TRECVID数据集上进行的实验表明，所公开的方法优于现有的几种重新排序方法。

35. 发明申请

US20110264641A1 IMAGE SEARCH RESULT SUMMARIZATION WITH INFORMATIVE PRIORS 有权
标题翻译：图像搜索结果与信息先驱者的概述
公开(公告)号：US20110264641A1
公开(公告)日：2011-10-27
申请号：US12764917
申请日：2010-04-21
申请人： Linjun Yang , Rui Liu , Xian-Sheng Hua
发明人： Linjun Yang , Rui Liu , Xian-Sheng Hua
IPC分类号： G06F17/30
CPC分类号： G06F17/3028
摘要： An informative priors image search result summarization system and method that summarizes image search results based on the image relevance (as determined by a search engine's initial ranking) and the image quality. Embodiments of the system and method cluster the image search results, rank images within each cluster based on a computed image score, and then select a summary image for the cluster. Each cluster is analyzed and an image in the cluster having the maximum image score is included in a selected summary collection. The image score is computed using the image relevance and the image quality, as well as a cluster coherence, a density, and a diversity. The selection of images from a collection of candidate images generates an image search result summarization, which is presented to a user. The summaries are presented to the user in a ranked order based on their image scores.
摘要翻译：一种信息先验图像搜索结果汇总系统和方法，其基于图像相关性（由搜索引擎的初始排名确定）和图像质量来总结图像搜索结果。系统和方法的实施例对图像搜索结果进行聚类，基于计算的图像分数对每个聚类内的图像进行排序，然后选择聚类的摘要图像。分析每个群集，并且具有最大图像得分的群集中的图像被包括在所选择的摘要集合中。使用图像相关性和图像质量以及簇相干性，密度和多样性来计算图像分数。来自候选图像集合的图像的选择产生呈现给用户的图像搜索结果汇总。这些摘要根据他们的图像分数以排序顺序呈现给用户。

36. 发明申请

US20100250190A1 TAG RANKING 有权
标题翻译： TAG排名
公开(公告)号：US20100250190A1
公开(公告)日：2010-09-30
申请号：US12415722
申请日：2009-03-31
申请人： HongJiang Zhang , Dong Liu , Meng Wang , Linjun Yang , Xian-Sheng Hua
发明人： HongJiang Zhang , Dong Liu , Meng Wang , Linjun Yang , Xian-Sheng Hua
IPC分类号： G06F17/18
CPC分类号： G06F17/30038
摘要： Technologies for generating a boosted tag ranking for a media instance, the boosted tag ranking based on probabilistic relevance estimation and tag correlation refining. Such boosted tag rankings may be used for search result ranking, tag recommendation, and group recommendation.
摘要翻译：用于生成媒体实例的增强标签排名的技术，基于概率相关性估计和标签相关性提炼的提升标签排名。这种提升的标签排名可以用于搜索结果排名，标签推荐和组推荐。

37. 发明申请

US20100217732A1 Unbiased Active Learning 有权
标题翻译：无偏见主动学习
公开(公告)号：US20100217732A1
公开(公告)日：2010-08-26
申请号：US12391511
申请日：2009-02-24
申请人： Linjun Yang , Bo Geng , Xian-Sheng Hua
发明人： Linjun Yang , Bo Geng , Xian-Sheng Hua
IPC分类号： G06F15/18
CPC分类号： G06N99/005
摘要： Techniques described herein create an accurate active-learning model that takes into account a sample selection bias of elements, such as images, selected for labeling by a user. These techniques select a first set of elements for labeling. Once a user labels these elements, the techniques calculate a sample selection bias of the selected elements and train a model that takes into account the sample selection bias. The techniques then select a second set of elements based, in part, on a sample selection bias of the elements. Again, once a user labels the second set of elements the techniques train the model while taking into account the calculated sample selection bias. Once the trained model satisfies a predefined stop condition, the techniques use the trained model to predict labels for the remaining unlabeled elements.
摘要翻译：本文描述的技术创建了一种精确的主动学习模型，其考虑了由用户选择进行标签选择的元素（例如图像）的样本选择偏差。这些技术选择用于标记的第一组元素。一旦用户标记了这些元素，这些技术就会计算所选元素的样本选择偏差，并训练考虑样本选择偏倚的模型。然后，技术部分地基于元素的样本选择偏差来选择第二组元素。同样，一旦用户标记第二组元素，则该技术训练模型，同时考虑计算的样本选择偏差。一旦训练的模型满足预定义的停止条件，该技术使用经过训练的模型来预测剩余的未标记元素的标签。

38. 发明申请

US20100205202A1 Visual and Textual Query Suggestion 有权
标题翻译：视觉和文本查询建议
公开(公告)号：US20100205202A1
公开(公告)日：2010-08-12
申请号：US12369421
申请日：2009-02-11
申请人： Linjun Yang , Meng Wang , Zhengjun Zha , Tao Mei , Xian-Sheng Hua
发明人： Linjun Yang , Meng Wang , Zhengjun Zha , Tao Mei , Xian-Sheng Hua
IPC分类号： G06F17/30
CPC分类号： G06F17/3064 , G06F17/30277 , G06F17/30864
摘要： Techniques described herein enable better understanding of the intent of a user that submits a particular search query. These techniques receive a search request for images associated with a particular query. In response, the techniques determine images that are associated with the query, as well as other keywords that are associated with these images. The techniques then cluster, for each set of images associated with one of these keywords, the set of images into multiple groups. The techniques then rank the images and determine a representative image of each cluster. Finally, the tools suggest, to the user that submitted the query, to refine the search based on user selection of a keyword and a representative image. Thus, the techniques better understand the user's intent by allowing the user to refine the search based on another keyword and based on an image on which the user wishes to focus the search.
摘要翻译：本文描述的技术能够更好地理解提交特定搜索查询的用户的意图。这些技术接收与特定查询相关联的图像的搜索请求。作为响应，这些技术确定与查询相关联的图像以及与这些图像相关联的其他关键词。然后，对于与这些关键词之一相关联的每组图像，该技术将该组图像聚类成多个组。然后，技术对图像进行排序并确定每个聚类的代表图像。最后，工具向提交查询的用户建议，根据用户对关键字和代表图像的选择来优化搜索。因此，这些技术通过允许用户基于另一个关键字来改进搜索并且基于用户希望集中搜索的图像来更好地理解用户的意图。

39. 发明授权

US08875007B2 Creating and modifying an image wiki page 有权
标题翻译：创建和修改图像维基页面
公开(公告)号：US08875007B2
公开(公告)日：2014-10-28
申请号：US12941739
申请日：2010-11-08
申请人： Linjun Yang , Qi Tian
发明人： Linjun Yang , Qi Tian
IPC分类号： G06F17/22 , G06F17/30
CPC分类号： G06F17/30277 , G06F17/3028 , G06F17/30893
摘要： An ImageWiki architecture is used to generate an image-based web page for an image on the Web. An ImageWiki page may be created automatically or individually, by a user of the Web. Additionally, a user may revise existing ImageWiki pages to update a particular page or correct an incorrect or misleading previous entry. The ImageWiki application indexes images located on the Web. Once the images are indexed, the information related to the images is mined and extracted from various sources of web data. Finally, an ImageWiki page or web page is generated for each image. The resulting ImageWiki page contains the image as well as the aggregated information relating to the image.
摘要翻译： ImageWiki架构用于为Web上的图像生成基于图像的网页。一个ImageWiki页面可以由Web的用户自动或单独创建。此外，用户可以修改现有的ImageWiki页面以更新特定页面或更正错误或误导的上一个条目。 ImageWiki应用程序对Web上的图像进行索引。一旦图像被索引，与图像相关的信息被挖掘并从各种数据来源提取。最后，为每个图像生成一个ImageWiki页面或网页。所得到的ImageWiki页面包含图像以及与图像相关的聚合信息。

40. 发明申请

US20140250115A1 Prototype-Based Re-Ranking of Search Results 审中-公开
标题翻译：基于原型的搜索结果重新排序
公开(公告)号：US20140250115A1
公开(公告)日：2014-09-04
申请号：US13395420
申请日：2011-11-21
申请人： Linjun Yang
发明人： Linjun Yang
IPC分类号： G06F17/30
CPC分类号： G06F16/24578 , G06F16/50 , G06F16/58 , G06F16/583
摘要： A prototype-based re-ranking method may re-rank search results to provide a re-ranked set of search results. In response to receiving one or more queries, a set of search results may be generated whereby each of the search results may be associated with a rank position. Based at least in part on the search results, one or more prototypes may be generated that visually represent the one or more queries or the search results. The one or more prototypes may be used to construct one or more meta re-rankers that may generate re-ranking scores for the search results. The re-ranking scores may be aggregated to produce a final relevance score for each search result included in the set of search results. Based at least in part on the relevance score of each search result and/or a learned re-ranking model, a set of re-ranked search results may be provided.
摘要翻译：基于原型的重新排序方法可以重新排列搜索结果以提供重新排列的搜索结果集合。响应于接收一个或多个查询，可以生成一组搜索结果，其中每个搜索结果可以与等级位置相关联。至少部分地基于搜索结果，可以生成可视地表示一个或多个查询或搜索结果的一个或多个原型。一个或多个原型可以用于构造一个或多个可以为搜索结果生成重新排序得分的元重新排序者。重新排名得分可以被聚合以产生包括在搜索结果集中的每个搜索结果的最终相关性得分。至少部分地基于每个搜索结果和/或学习的重新排序模型的相关性得分，可以提供一组重新排序的搜索结果。

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