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41. 发明授权

US08094872B1 Three-dimensional wavelet based video fingerprinting 有权
标题翻译：基于三维小波的视频指纹识别
公开(公告)号：US08094872B1
公开(公告)日：2012-01-10
申请号：US11746339
申请日：2007-05-09
申请人： Jay Yagnik , Henry A. Rowley , Sergey Ioffe
发明人： Jay Yagnik , Henry A. Rowley , Sergey Ioffe
IPC分类号： G06K9/00 , G06K9/46 , H04L9/32 , H04N7/167
CPC分类号： G06K9/00711 , H04N21/23418
摘要： A method and system generates and compares fingerprints for videos in a video library. The video fingerprints provide a compact representation of the spatial and sequential characteristics of the video that can be used to quickly and efficiently identify video content. Because the fingerprints are based on spatial and sequential characteristics rather than exact bit sequences, visual content of videos can be effectively compared even when there are small differences between the videos in compression factors, source resolutions, start and stop times, frame rates, and so on. Comparison of video fingerprints can be used, for example, to search for and remove copyright protected videos from a video library. Further, duplicate videos can be detected and discarded in order to preserve storage space.
摘要翻译：方法和系统生成并比较视频库中视频的指纹。视频指纹提供了可用于快速有效地识别视频内容的视频的空间和顺序特征的紧凑表示。因为指纹是基于空间和顺序特征而不是精确的比特序列，所以即使在压缩因素，源分辨率，开始和停止时间，帧率等之间的视频之间存在小的差异，也可以有效地比较视频的视觉内容上。可以使用比较视频指纹，例如，从视频库搜索和删除受版权保护的视频。此外，为了保存存储空间，可以检测和丢弃重复的视频。

42. 发明授权

US08001062B1 Supervised learning using multi-scale features from time series events and scale space decompositions 有权
标题翻译：使用时间序列事件和尺度空间分解的多尺度特征进行监督学习
公开(公告)号：US08001062B1
公开(公告)日：2011-08-16
申请号：US11952436
申请日：2007-12-07
申请人： Ullas Gargi , Jay Yagnik
发明人： Ullas Gargi , Jay Yagnik
IPC分类号： G06F11/00
CPC分类号： G06K9/00536 , G06K9/00516
摘要： Disclosed herein is a method, a system and a computer program product for generating a statistical classification model used by a computer system to determine a class associated with an unlabeled time series event. Initially, a set of labeled time series events is received. A set of time series features is identified for a selected set of the labeled time series events. A plurality of scale space decompositions is generated based on the set of time series features. A plurality of multi-scale features is generated based on the plurality of scale space decompositions. A first subset of the plurality of multi-scale features that correspond at least in part to a subset of space or time points within a time series event that contain feature data that distinguish the time series event as belonging to a class of time series events that corresponds to the class label are identified. A statistical classification model for classifying an unlabeled time series event based on the class corresponding with the class label is generated based at least in part on the at the first subset of the plurality of multi-scale features.
摘要翻译：本文公开了一种用于生成由计算机系统用于确定与未标记的时间序列事件相关联的类别的统计分类模型的方法，系统和计算机程序产品。最初，接收一组标记的时间序列事件。针对所选择的一组标记的时间序列事件识别一组时间序列特征。基于该组时间序列特征生成多个比例空间分解。基于多个刻度空间分解产生多个多尺度特征。所述多个多尺度特征的第一子集至少部分对应于时间序列事件内的空间或时间点的子集，所述时间序列事件包含将所述时间序列事件区分为属于一类时间序列事件的特征数据，所述时间序列事件对应于类标签被识别。至少部分地基于多个多尺度特征的第一子集来生成用于基于与类标签相对应的类来分类未标记时间序列事件的统计分类模型。

43. 发明申请

US20100191689A1 VIDEO CONTENT ANALYSIS FOR AUTOMATIC DEMOGRAPHICS RECOGNITION OF USERS AND VIDEOS 审中-公开
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：US20100191689A1
公开(公告)日：2010-07-29
申请号：US12392987
申请日：2009-02-25
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： G06N5/02 , H04N7/10
CPC分类号： H04N21/2407 , G06F16/735 , G06F16/78 , G06F16/783 , G06F16/7867 , 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。

44. 发明申请

US20080130960A1 Identifying Images Using Face Recognition 有权
标题翻译：使用人脸识别识别图像
公开(公告)号：US20080130960A1
公开(公告)日：2008-06-05
申请号：US11566075
申请日：2006-12-01
申请人： Jay Yagnik
发明人： Jay Yagnik
IPC分类号： G06K9/62
CPC分类号： G06K9/00288 , G06F17/30247 , G06F17/30256 , G06K9/6256
摘要： A method includes identifying a named entity, retrieving images associated with the named entity, and using a face detection algorithm to perform face detection on the retrieved images to detect faces in the retrieved images. At least one representative face image from the retrieved images is identified, and the representative face image is used to identify one or more additional images representing the at least one named entity.
摘要翻译：一种方法包括识别命名实体，检索与命名实体相关联的图像，以及使用面部检测算法对检索到的图像执行面部检测，以检测检索到的图像中的面部。识别来自检索到的图像的至少一个代表性面部图像，并且使用代表性面部图像来识别表示至少一个命名实体的一个或多个附加图像。

45. 发明授权

US09367612B1 Correlation-based method for representing long-timescale structure in time-series data 有权
标题翻译：用于表示时间序列数据中的长时间尺度结构的相关方法
公开(公告)号：US09367612B1
公开(公告)日：2016-06-14
申请号：US13300057
申请日：2011-11-18
申请人： Douglas Eck , Jay Yagnik
发明人： Douglas Eck , Jay Yagnik
IPC分类号： G06F17/00 , G06F17/30
CPC分类号： G06F17/30743 , G06F17/30551 , G06F17/3074
摘要： A system identifies a set of initial segments of a time-based data item, such as audio. The segments can be defined at regular time intervals within the time-based data item. The initial segments are short segments. The system computes a short-timescale vectorial representation for each initial segment and compares the short-timescale vectorial representation for each initial segment with other short-timescale vectorial representations of the segments in a time duration within the time-based data item (e.g., audio) immediately preceding or immediately following the initial segment. The system generates a representation of long-timescale information for the time-based data item based on a comparison of the short-timescale vectorial representations of the initial segments and the short-timescale vectorial representations of immediate segments. The representation of long-timescale information identifies an underlying repetition structure of the time-based data item, such as rhythm or phrasing in an audio item.
摘要翻译：系统识别基于时间的数据项（例如音频）的一组初始段。可以在基于时间的数据项中以规则的时间间隔定义段。初始段是短段。该系统计算每个初始段的短时刻矢量表示，并将每个初始段的短时刻矢量表示与时间数据项内的时间段内的段的其他短时刻矢量表示进行比较（例如，音频）紧接在初始段之前或之后。基于初始段的短时刻矢量表示与即时段的短时刻矢量表示的比较，该系统生成用于基于时间的数据项的长时间尺度信息的表示。长时间尺度信息的表示识别基于时间的数据项的基础重复结构，例如音频项目中的节奏或短语。

46. 发明授权

US08959540B1 Predicting engagement in video content 有权
标题翻译：预测参与视频内容
公开(公告)号：US08959540B1
公开(公告)日：2015-02-17
申请号：US12783524
申请日：2010-05-19
申请人： Ullas Gargi , Jay Yagnik , Anindya Sarkar
发明人： Ullas Gargi , Jay Yagnik , Anindya Sarkar
IPC分类号： H04N7/16 , H04H60/32
CPC分类号： H04N21/23418 , H04H20/93 , H04H60/31 , H04H60/46 , H04H60/59 , H04H60/63 , H04N21/237 , H04N21/251 , H04N21/25891 , H04N21/44008 , H04N21/44222 , H04N21/466 , H04N21/4826 , H04N21/6582
摘要： User engagement in unwatched videos is predicted by collecting and aggregating data describing user engagement with watched videos. The data are normalized to reduce the influence of factors other than the content of the videos on user engagement. Engagement metrics are calculated for segments of watched videos that indicate user engagement with each segment relative to overall user engagement with the watched videos. Features of the watched videos within time windows are characterized, and a function is learned that relates the features of the videos within the time windows to the engagement metrics for the time windows. The features of a time window of an unwatched video are characterized, and the learned function is applied to the features to predict user engagement to the time window of the unwatched video. The unwatched video can be enhanced based on the predicted user engagement.
摘要翻译：通过收集和汇总描述与观看视频的用户互动的数据来预测用户对未拍摄的视频的参与。数据被归一化以减少视频内容以外的因素对用户参与的影响。针对所观看视频的细分，计算参与指标，指示用户与每个细分受众群相对于与观看视频的整体用户互动度的参与度。在时间窗口中观看视频的特征被描述，并且学习了将时间窗口内的视频的特征与时间窗口的参与度量相关联的功能。对未拍摄视频的时间窗口的特征进行了表征，并且将学习的功能应用于特征以预测用户对未拍摄视频的时间窗口的参与。可以根据预测的用户参与来增强未拍摄的视频。

47. 发明授权

US08898148B1 Targeting to physical environment 有权
标题翻译：针对物理环境
公开(公告)号：US08898148B1
公开(公告)日：2014-11-25
申请号：US13443079
申请日：2012-04-10
申请人： Jay Yagnik , Niyati Yagnik
发明人： Jay Yagnik , Niyati Yagnik
IPC分类号： G06F17/30
CPC分类号： G06F17/30867
摘要： A computer-implemented information targeting method is disclosed. The method includes receiving a search query from a computing device, where the search query has at least two different meanings, identifying metadata associated with the search query, using the metadata to promote search results corresponding to a first meaning of the at least two meanings of the search query, and providing search results corresponding to the first meaning of the search query to the computing device. Using the metadata to promote search results may comprise analyzing (a) prior search queries that are related to the received search query, (b) metadata associated with the prior search queries, and (c) selections of search results provided in response to the prior search queries; and identifying a correlation between the metadata associated with the prior search queries and selections of search results presented in response to the prior search queries.
摘要翻译：公开了一种计算机实现的信息定位方法。该方法包括从计算设备接收搜索查询，其中搜索查询具有至少两个不同含义，识别与搜索查询相关联的元数据，使用元数据来促进对应于至少两个含义的第一含义的搜索结果搜索查询，并且将与搜索查询的第一含义相对应的搜索结果提供给计算设备。使用元数据来促进搜索结果可以包括分析（a）与所接收的搜索查询相关的先前搜索查询，（b）与先前搜索查询相关联的元数据，以及（c）响应于先前提供的搜索结果的选择搜索查询; 以及识别与先前搜索查询相关联的元数据与响应于先前搜索查询呈现的搜索结果的选择之间的相关性。

48. 发明授权

US08880415B1 Hierarchical encoding of time-series data features 有权
标题翻译：时间序列数据特征的分层编码
公开(公告)号：US08880415B1
公开(公告)日：2014-11-04
申请号：US13316286
申请日：2011-12-09
申请人： Douglas Eck , Jay Yagnik
发明人： Douglas Eck , Jay Yagnik
IPC分类号： G10L21/00
CPC分类号： G10L25/27 , G10H2250/581 , G10L2019/0001
摘要： A computing device identifies a first codeword in a first codebook to represent short-timescale information of frames in a time-based data item segmented at intervals and identifies a second codeword in a second codebook to represent long-timescale information of the frames. The computing device generates a third codebook based on the first codeword and the second codeword for the frames to add long-timescale information context to the short-timescale information of the frames.
摘要翻译：计算设备识别第一码本中的第一码字以表示以间隔分段的基于时间的数据项中的帧的短时间尺度信息，并且识别第二码本中的第二码字以表示帧的长时间尺度信息。计算设备基于第一码字和第二码字生成第三码本，以便为帧的短时间尺度信息添加长时间尺度信息上下文。

49. 发明授权

US08819024B1 Learning category classifiers for a video corpus 有权
标题翻译：学习视频语料库的类别分类器
公开(公告)号：US08819024B1
公开(公告)日：2014-08-26
申请号：US12950955
申请日：2010-11-19
申请人： George Toderici , Hrishikesh Aradhye , Alexandru Marius Pasca , Luciano Sbaiz , Jay Yagnik
发明人： George Toderici , Hrishikesh Aradhye , Alexandru Marius Pasca , Luciano Sbaiz , Jay Yagnik
IPC分类号： G06F7/00 , G06F17/30
CPC分类号： G06F17/30731 , G06K9/00718
摘要： A classifier training system learns classifiers for categories by combining data from a category-instance repository comprising relationships between categories and more specific instances of those categories with a set of video classifiers for different concepts. The category-instance repository is derived from the domain of textual documents, such as web pages, and the concept classifiers are derived from the domain of video. Taken together, the category-instance repository and the concept classifiers provide sufficient data for obtaining accurate classifiers for categories that encompass other lower-level concepts, where the categories and their classifiers may not be obtainable solely from the video domain.
摘要翻译：分类器训练系统通过将包括类别之间的关系的类别实例存储库的数据与这些类别的更具体的实例组合用于不同概念的一组视频分类器来学习类别的分类器。类别实例存储库是从文本文档的领域（如Web页面）导出的，概念分类器是从视频域派生的。总而言之，类别实例存储库和概念分类器提供足够的数据，以便为包含其他较低级别概念的类别获取准确的分类器，其中类别及其分类可能无法仅从视频域获得。

50. 发明授权

US08792732B1 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：US08792732B1
公开(公告)日：2014-07-29
申请号：US13559420
申请日：2012-07-26
申请人： Ming Zhao , Jay Yagnik
发明人： Ming Zhao , Jay Yagnik
IPC分类号： G06K9/46 , H04N5/14
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。

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