专利快速检索-快速检索全球专利，免费商用专利数据库-IPRDB

61. 发明授权

US08768048B1 System and method for exploiting segment co-occurrence relationships to identify object location in images 有权
标题翻译：利用片段同现关系识别图像中对象位置的系统和方法
公开(公告)号：US08768048B1
公开(公告)日：2014-07-01
申请号：US13299789
申请日：2011-11-18
申请人： Vivek Kwatra , Jay Yagnik , Alexander T. Toshev , Poonam Suryanarayan
发明人： Vivek Kwatra , Jay Yagnik , Alexander T. Toshev , Poonam Suryanarayan
IPC分类号： G06K9/62
CPC分类号： G06K9/6202 , G06K9/72
摘要： A computing device segments an image into a plurality of segments, wherein each segment of the plurality of segments comprises a set of pixels that share visual characteristics. The computing device then determines expected contexts for the segments, wherein an expected context for a segment comprises at least one of additional segments or features expected to occur in the image together with the segment. The computing device then identifies a probable object based on the expected contexts.
摘要翻译：计算设备将图像分割成多个段，其中多个段中的每个段包括共享视觉特征的一组像素。计算设备然后确定段的预期上下文，其中段的预期上下文包括期望与该段一起出现在图像中的附加段或特征中的至少一个。然后，计算设备基于预期上下文识别可能的对象。

62. 发明授权

US08738633B1 Transformation invariant media matching 有权
标题翻译：转换不变媒体匹配
公开(公告)号：US08738633B1
公开(公告)日：2014-05-27
申请号：US13362905
申请日：2012-01-31
申请人： Matthew Sharifi , Sergey Ioffe , Jay Yagnik , Gheorghe Postelnicu , Dominik Roblek , George Tzanetakis
发明人： Matthew Sharifi , Sergey Ioffe , Jay Yagnik , Gheorghe Postelnicu , Dominik Roblek , George Tzanetakis
IPC分类号： G06F17/30
CPC分类号： G06K9/6267 , G06F17/3002 , G06F17/30244 , G06K9/00013
摘要： This disclosure relates to transformation invariant media matching. A fingerprinting component can generate a transformation invariant identifier for media content by adaptively encoding the relative ordering of interest points in media content. The interest points can be grouped into subsets, and stretch invariant descriptors can be generated for the subsets based on ratios of coordinates of interest points included in the subsets. The stretch invariant descriptors can be aggregated into a transformation invariant identifier. An identification component compares the identifier against a set of identifiers for known media content, and the media content can be matched or identified as a function of the comparison.
摘要翻译：本公开涉及变换不变媒体匹配。指纹分量可以通过对媒体内容中的兴趣点的相对排序进行自适应编码来生成媒体内容的变换不变标识符。可以将兴趣点分组为子集，并且可以基于子集中包括的兴趣点坐标的比例为子集生成拉伸不变描述符。拉伸不变描述符可以聚合成变换不变标识符。识别部件将标识符与已知媒体内容的一组标识符进行比较，并且媒体内容可以作为比较的函数进行匹配或标识。

63. 发明授权

US08676725B1 Method and system for entropy-based semantic hashing 有权
标题翻译：基于熵的语义散列的方法和系统
公开(公告)号：US08676725B1
公开(公告)日：2014-03-18
申请号：US12794380
申请日：2010-06-04
申请人： Ruei-Sung Lin , David Ross , Jay Yagnik
发明人： Ruei-Sung Lin , David Ross , Jay Yagnik
IPC分类号： G06F15/18
CPC分类号： G06N99/005
摘要： Methods, systems and articles of manufacture for identifying semantic nearest neighbors in a feature space are described herein. A method embodiment includes generating an affinity matrix for objects in a given feature space, wherein the affinity matrix identifies the semantic similarity between each pair of objects in the feature space, training a multi-bit hash function using a greedy algorithm that increases the Hamming distance between dissimilar objects in the feature space while minimizing the Hamming distance between similar objects, and identifying semantic nearest neighbors for an object in a second feature space using the multi-bit hash function. A system embodiment includes a hash generator configured to generate the affinity matrix and train the multi-bit hash function, and a similarity determiner configured to identify semantic nearest neighbors for an object in a second feature space using the multi-bit hash function.
摘要翻译：本文描述了用于识别特征空间中的语义最近邻居的方法，系统和制品。方法实施例包括为给定特征空间中的对象生成亲和度矩阵，其中亲和矩阵识别特征空间中每对对象之间的语义相似性，使用增加汉明距离的贪心算法训练多比特哈希函数在特征空间中的不相似对象之间，同时使相似对象之间的汉明距离最小化，并且使用多位哈希函数来识别第二特征空间中的对象的语义最近邻居。系统实施例包括被配置为生成亲和度矩阵并训练多比特哈希函数的哈希发生器，以及被配置为使用多比特哈希函数来识别第二特征空间中的对象的语义最近邻居的相似性确定器。

64. 发明授权

US08589457B1 Training scoring models optimized for highly-ranked results 有权
公开(公告)号：US08589457B1
公开(公告)日：2013-11-19
申请号：US13616108
申请日：2012-09-14
申请人： Samy Bengio , Gal Chechik , Sergey Ioffe , Jay Yagnik
发明人： Samy Bengio , Gal Chechik , Sergey Ioffe , Jay Yagnik
IPC分类号： G06F17/00
CPC分类号： G06K9/66 , G06F17/30244 , G06F17/3053 , G06K9/6267 , Y10S707/913 , Y10S707/915
摘要： Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training scoring models. One method includes storing data identifying a plurality of positive and a plurality of negative training images for a query. The method further includes selecting a first image from either the positive group of images or the negative group of images, and applying a scoring model to the first image. The method further includes selecting a plurality of candidate images from the other group of images, applying the scoring model to each of the candidate images, and then selecting a second image from the candidate images according to scores for the images. The method further includes determining that the scores for the first image and the second image fail to satisfy a criterion, updating the scoring model, and storing the updated scoring model.

65. 发明授权

US08533236B1 Video-related recommendations using link structure 有权
标题翻译：视频相关建议使用链接结构
公开(公告)号：US08533236B1
公开(公告)日：2013-09-10
申请号：US13559302
申请日：2012-07-26
申请人： Shumeet Baluja , Yushi Jing , Dandapani Sivakumar , Jay Yagnik
发明人： Shumeet Baluja , Yushi Jing , Dandapani Sivakumar , Jay Yagnik
IPC分类号： G06F17/30
CPC分类号： G06F17/30035 , G06F17/30828 , G06Q30/02 , Y10S707/99942 , Y10S707/99945
摘要： The subject matter of this specification can be embodied in, among other things, a method that includes inferring labels for videos, users, advertisements, groups of users, and other entities included in a social network system. The inferred labels can be used to generate recommendations such as videos or advertisements in which a user may be interested. Inferred labels can be generated based on social or other relationships derived from, for example, profiles or activities of social network users. Inferred labels can be advantageous when explicit information about these entities is not available. For example, a particular user may not have clicked on any online advertisements, so the user is not explicitly linked to any advertisements.
摘要翻译：本说明书的主题可以包括推断用于视频，用户，广告，用户组以及包括在社交网络系统中的其他实体的标签的方法。推断的标签可用于生成用户可能感兴趣的建议，如视频或广告。可以基于从例如社交网络用户的简档或活动导出的社会或其他关系来生成推断标签。当关于这些实体的显式信息不可用时，推断的标签可能是有利的。例如，特定用户可能没有点击任何在线广告，因此用户没有明确地链接到任何广告。

66. 发明授权

US08473500B2 Inferring user interests 有权
标题翻译：推荐用户兴趣
公开(公告)号：US08473500B2
公开(公告)日：2013-06-25
申请号：US13290455
申请日：2011-11-07
申请人： Shumeet Baluja , Yushi Jing , Dandapani Sivakumar , Jay Yagnik
发明人： Shumeet Baluja , Yushi Jing , Dandapani Sivakumar , Jay Yagnik
IPC分类号： G06F17/30 , G06F15/16
CPC分类号： G06N5/02 , G06N99/005 , G06Q30/0274
摘要： The subject matter of this specification can be embodied in, among other things, a method that includes determining, for a portion of users of a social network, label values each comprising an inferred interest level of a user in a subject indicated by a label, associating a first user with one or more second users based on one or more relationships specified by the first user, and outputting a first label value for the first user based on one or more second label values of the one or more second users.
摘要翻译：本说明书的主题可以包括一种方法，其包括为社交网络的用户的一部分确定每个包括由标签指示的对象中的用户的推定的兴趣级别的标签值，基于由第一用户指定的一个或多个关系将第一用户与一个或多个第二用户相关联，并且基于一个或多个第二用户的一个或多个第二标签值输出第一用户的第一标签值。

67. 发明授权

US08452778B1 Training of adapted classifiers for video categorization 有权
标题翻译：适应分类器的视频分类培训
公开(公告)号：US08452778B1
公开(公告)日：2013-05-28
申请号：US12874015
申请日：2010-09-01
申请人： Yang Song , Ming Zhao , Jay Yagnik
发明人： Yang Song , Ming Zhao , Jay Yagnik
IPC分类号： G06F17/30 , G10L19/12
CPC分类号： G06F17/30799 , G06F17/30796 , G06K9/00711
摘要： A classifier training system trains adapted classifiers for classifying videos based at least in part on scores produced by application of text-based classifiers to textual metadata of the videos. Each classifier corresponds to a particular category, and when applied to a given video indicates whether the video represents the corresponding category. The classifier training system applies the text-based classifiers to textual metadata of the videos to obtain the scores, and also extracts features from content of the videos, combining the scores and the content features for a video into a set of hybrid features. The adapted classifiers are then trained on the hybrid features. The adaption of the text-based classifiers from the textual domain to the video domain allows the training of accurate video classifiers (the adapted classifiers) without requiring a large training set of authoritatively labeled videos.
摘要翻译：分类器训练系统训练适用的分类器，用于至少部分地基于将基于文本的分类器应用于视频的文本元数据而产生的分数来分类视频。每个分类器对应于特定类别，并且当应用于给定视频时指示视频是否表示相应类别。分类器训练系统将基于文本的分类器应用于视频的文本元数据以获得分数，并且还从视频内容中提取特征，将视频的分数和内容特征组合成一组混合特征。然后对适应的分类器对混合特征进行训练。基于文本的分类器从文本域到视频域的适应允许训练准确的视频分类器（适应的分类器），而不需要大量的授权标签视频的训练集。

68. 发明申请

US20130117780A1 VIDEO SYNTHESIS USING VIDEO VOLUMES 有权
标题翻译：视频合成使用视频卷
公开(公告)号：US20130117780A1
公开(公告)日：2013-05-09
申请号：US13633067
申请日：2012-10-01
申请人： RAHUL SUKTHANKAR , JAY YAGNIK
发明人： RAHUL SUKTHANKAR , JAY YAGNIK
IPC分类号： H04N21/236
CPC分类号： G06K9/00744 , G06F17/3079 , G06F17/3082 , G06K9/00718 , G06T9/00
摘要： A volume identification system identifies a set of unlabeled spatio-temporal volumes within each of a set of videos, each volume representing a distinct object or action. The volume identification system further determines, for each of the videos, a set of volume-level features characterizing the volume as a whole. In one embodiment, the features are based on a codebook and describe the temporal and spatial relationships of different codebook entries of the volume. The volume identification system uses the volume-level features, in conjunction with existing labels assigned to the videos as a whole, to label with high confidence some subset of the identified volumes, e.g., by employing consistency learning or training and application of weak volume classifiers. The labeled volumes may be used for a number of applications, such as training strong volume classifiers, improving video search (including locating individual volumes), and creating composite videos based on identified volumes.
摘要翻译：体积识别系统识别一组视频中的每一个中的一组未标记的时空体积，每个体积表示不同的对象或动作。音量识别系统进一步为每个视频确定表征整个音量的一组音量级特征。在一个实施例中，特征基于码本并且描述卷的不同码本条目的时间和空间关系。音量识别系统使用音量级特征，结合分配给整个视频的现有标签，以高度置信的方式标识所识别的体积的一些子集，例如通过采用一致性学习或训练和应用弱音量分类器。标记的卷可以用于许多应用，例如训练强大的分类器，改进视频搜索（包括定位各个卷），以及基于识别的卷创建复合视频。

69. 发明申请

US20120272259A1 VIDEO CONTENT ANALYSIS FOR AUTOMATIC DEMOGRAPHICS RECOGNITION OF USERS AND VIDEOS 有权
标题翻译：视频内容分析用于自动人脸识别用户和视频
公开(公告)号：US20120272259A1
公开(公告)日：2012-10-25
申请号：US13488126
申请日：2012-06-04
申请人： Corinna Cortes , Sanjiv Kumar , Ameesh Makadia , Gideon Mann , Jay Yagnik , Ming Zhao
发明人： Corinna Cortes , Sanjiv Kumar , Ameesh Makadia , Gideon Mann , Jay Yagnik , Ming Zhao
IPC分类号： H04N21/24
CPC分类号： H04N21/2407 , G06F17/30784 , G06F17/30817 , G06F17/3082 , G06F17/30828 , G06K9/00711 , H04N21/23418 , H04N21/25883 , H04N21/4668 , H04N21/4826
摘要： A video demographics analysis system selects a training set of videos to use to correlate viewer demographics and video content data. The video demographics analysis system extracts demographic data from viewer profiles related to videos in the training set and creates a set of demographic distributions, and also extracts video data from videos in the training set. The video demographics analysis system correlates the viewer demographics with the video data of videos viewed by that viewer. Using the prediction model produced by the machine learning process, a new video about which there is no a priori knowledge can be associated with a predicted demographic distribution specifying probabilities of the video appealing to different types of people within a given demographic category, such as people of different ages within an age demographic category.
摘要翻译：视频人口统计分析系统选择用于将观众人口特征和视频内容数据相关联的一组视频。视频人口统计分析系统从与训练集中的视频相关的观众简档中提取人口统计学数据，并创建一组人口分布，并从训练集中的视频中提取视频数据。视频人口统计分析系统将观众人口统计学与观众观看的视频的视频数据相关联。使用机器学习过程产生的预测模型，可以将预测的人口分布与预测的人口分布相关联，所述预测人口统计分布规定了给定人口统计学类别中的不同类型的人的视频的概率，例如人在不同年龄的人口统计学类别。

70. 发明授权

US08254699B1 Automatic large scale video object recognition 有权
标题翻译：自动大规模视频对象识别
公开(公告)号：US08254699B1
公开(公告)日：2012-08-28
申请号：US12364390
申请日：2009-02-02
申请人： Ming Zhao , Jay Yagnik
发明人： Ming Zhao , Jay Yagnik
IPC分类号： G06K9/46 , G06K9/48 , H04N5/14 , G06E1/00
CPC分类号： G06K9/6232 , G06K9/6215
摘要： An object recognition system performs a number of rounds of dimensionality reduction and consistency learning on visual content items such as videos and still images, resulting in a set of feature vectors that accurately predict the presence of a visual object represented by a given object name within an visual content item. The feature vectors are stored in association with the object name which they represent and with an indication of the number of rounds of dimensionality reduction and consistency learning that produced them. The feature vectors and the indication can be used for various purposes, such as quickly determining a visual content item containing a visual representation of a given object name.
摘要翻译：对象识别系统对诸如视频和静止图像的视觉内容项目执行多次维数降低和一致性学习，导致一组特征向量，其精确地预测由一个对象名称表示的视觉对象的存在视觉内容项目。特征向量与它们所代表的对象名称相关联地存储，并且显示产生它们的维度降低和一致性学习的轮次数。特征向量和指示可以用于各种目的，诸如快速确定包含给定对象名称的视觉表示的视觉内容项。

你已经成功收藏专利！

检索式保存成功!

IPRDB

热门服务

关于我们

友情链接

联系方式