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公开(公告)号：US10390067B1
公开(公告)日：2019-08-20
申请号：US15593448
申请日：2017-05-12
Applicant: Google Inc.
Inventor： Sanketh Shetty , Apostol Natsev , Balakrishnan Varadarajan , Tomas Izo
IPC: G06K9/00 , H04N21/262 , H04N21/61 , H04N21/234 , H04N21/258 , H04N21/45
Abstract: Implementations disclose predicting video start times for maximizing user engagement. A method includes receiving a first content item comprising content item segments, processing the first content item using a trained machine learning model that is trained based on interaction signals and audio-visual content features of a training set of training segments of training content items, and obtaining, based on the processing of the first content item using the trained machine learning model, one or more outputs comprising salience scores for the content item segments, the salience scores indicating which content item segment of the content item segments is to be selected as a starting point for playback of the first content item.
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公开(公告)号：US09659218B1
公开(公告)日：2017-05-23
申请号：US14699243
申请日：2015-04-29
Applicant: Google Inc.
Inventor： Sanketh Shetty , Apostol Natsev , Balakrishnan Varadarajan , Tomas Izo
CPC classification number: G06K9/00744
Abstract: Implementations disclose predicting video start times for maximizing user engagement. A method includes applying a machine-learned model to audio-visual content features of segments of a target content item, the machine-learned model trained based on user interaction signals and audio-visual content features of a training set of content item segments, calculating, based on applying the machine-learned model, a salience score for each of the segments of the target content item, and selecting, based on the calculated salience scores, one of the segments of the target content item as a starting point for playback of the target content item.
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公开(公告)号：US20160335499A1
公开(公告)日：2016-11-17
申请号：US14712071
申请日：2015-05-14
Applicant: Google Inc.
Inventor： Min-hsuan Tsai , Sudheendra Vijayanarasimhan , Tomas Izo , Sanketh Shetty , Balakrishnan Varadarajan
CPC classification number: G06K9/00765 , G06K9/6269 , G06K9/66 , H04N5/91
Abstract: A solution is provided for temporally segmenting a video based on analysis of entities identified in the video frames of the video. The video is decoded into multiple video frames and multiple video frames are selected for annotation. The annotation process identifies entities present in a sample video frame and each identified entity has a timestamp and confidence score indicating the likelihood that the entity is accurately identified. For each identified entity, a time series comprising of timestamps and corresponding confidence scores is generated and smoothed to reduce annotation noise. One or more segments containing an entity over the length of the video are obtained by detecting boundaries of the segments in the time series of the entity. From the individual temporal segmentation for each identified entity in the video, an overall temporal segmentation for the video is generated, where the overall temporal segmentation reflects the semantics of the video.
Abstract translation: 提供了一种解决方案，用于基于在视频的视频帧中识别的实体的分析来对视频进行时间分割。 将视频解码为多个视频帧，并选择多个视频帧进行注释。 注释过程识别存在于样本视频帧中的实体，并且每个识别的实体具有指示实体准确识别的可能性的时间戳和置信度分数。 对于每个识别的实体，产生并平滑包括时间戳和对应的置信度分数的时间序列以减少注释噪声。 通过检测实体的时间序列中的段的边界来获得包含视频长度上的实体的一个或多个段。 根据视频中每个被识别实体的个体时间分割，生成视频的总体时间分割，其中整体时间分段反映视频的语义。
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公开(公告)号：US20160070962A1
公开(公告)日：2016-03-10
申请号：US14848216
申请日：2015-09-08
Applicant: Google Inc.
Inventor： Sanketh Shetty , Tomas Izo , Min-Hsuan Tsai , Sudheendra Vijayanarasimhan , Apostol Natsev , Sami Abu-El-Haija , George Dan Toderici , Susanna Ricco , Balakrishnan Varadarajan , Nicola Muscettola , WeiHsin Gu , Weilong Yang , Nitin Khandelwal , Phuong Le
CPC classification number: G06K9/00718 , G06F17/30787 , G06K9/00744 , G06K9/00751 , G06K9/00765 , G06K2209/27
Abstract: A computer-implemented method for selecting representative frames for videos is provided. The method includes receiving a video and identifying a set of features for each of the frames of the video. The features including frame-based features and semantic features. The semantic features identifying likelihoods of semantic concepts being present as content in the frames of the video. A set of video segments for the video is subsequently generated. Each video segment includes a chronological subset of frames from the video and each frame is associated with at least one of the semantic features. The method generates a score for each frame of the subset of frames for each video segment based at least on the semantic features, and selecting a representative frame for each video segment based on the scores of the frames in the video segment. The representative frame represents and summarizes the video segment.
Abstract translation: 提供了一种用于选择视频的代表性帧的计算机实现的方法。 该方法包括接收视频并识别视频的每个帧的一组特征。 特征包括基于帧的特征和语义特征。 识别语义概念的可能性的语义特征作为视频帧中的内容呈现。 随后生成视频的一组视频段。 每个视频段包括来自视频的帧的按时间顺序的子集，并且每个帧与语义特征中的至少一个相关联。 该方法至少基于语义特征为每个视频段的帧子集的每帧生成分数，并且基于视频段中的帧的分数为每个视频段选择代表性的帧。 代表性的框架代表和总结视频段。
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公开(公告)号：US20170046573A1
公开(公告)日：2017-02-16
申请号：US14823946
申请日：2015-08-11
Applicant: Google Inc.
Inventor： Balakrishnan Varadarajan , George Dan Toderici , Apostol Natsev , Nitin Khandelwal , Sudheendra Vijayanarasimhan , Weilong Yang , Sanketh Shetty
CPC classification number: G06K9/00718 , G06F17/30784 , G06F17/3082 , G06K9/52 , G06K9/6201 , G06K9/6256 , G06K9/627 , H04N5/265
Abstract: A system and methodology provide for annotating videos with entities and associated probabilities of existence of the entities within video frames. A computer-implemented method identifies an entity from a plurality of entities identifying characteristics of video items. The computer-implemented method selects a set of features correlated with the entity based on a value of a feature of a plurality of features, determines a classifier for the entity using the set of features, and determines an aggregation calibration function for the entity based on the set of features. The computer-implemented method selects a video frame from a video item, where the video frame having associated features, and determines a probability of existence of the entity based on the associated features using the classifier and the aggregation calibration function.
Abstract translation: 系统和方法提供用实体注释视频和在视频帧内存在实体的相关概率。 计算机实现的方法从识别视频项目的特征的多个实体中识别实体。 计算机实现的方法基于多个特征的特征的值来选择与实体相关的一组特征，使用该特征集来确定实体的分类器，并且基于该特征确定该实体的聚合校准功能 的功能集。 计算机实现的方法从视频项目中选择视频帧，其中具有相关联特征的视频帧，并且使用分类器和聚合校准功能基于相关联的特征来确定实体的存在概率。
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Patent Agency Ranking
公开(公告)号：US11328218B1
公开(公告)日：2022-05-10
申请号：US15805060
申请日：2017-11-06
Applicant: Google Inc.
Inventor： Hrishikesh Aradhye , Sanketh Shetty
IPC: G06N20/00 , G06F16/2457
Abstract: A system and method for identifying and predicting subjective attributes for entities (e.g., media clips, movies, television shows, images, newspaper articles, blog entries, persons, organizations, commercial businesses, etc.) are disclosed. In one aspect, subjective attributes for a first media item are identified based on a reaction to the first media item, and relevancy scores for the subjective attributes with respect to the first media item are determined. A classifier is trained using (i) a training input comprising a set of features for the first media item, and a target output for the training input, the target output comprising the respective relevancy scores for the subjective attributes with respect to the first media item.
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公开(公告)号：US09607224B2
公开(公告)日：2017-03-28
申请号：US14712071
申请日：2015-05-14
Applicant: Google Inc.
Inventor： Min-hsuan Tsai , Sudheendra Vijayanarasimhan , Tomas Izo , Sanketh Shetty , Balakrishnan Varadarajan
CPC classification number: G06K9/00765 , G06K9/6269 , G06K9/66 , H04N5/91
Abstract: A solution is provided for temporally segmenting a video based on analysis of entities identified in the video frames of the video. The video is decoded into multiple video frames and multiple video frames are selected for annotation. The annotation process identifies entities present in a sample video frame and each identified entity has a timestamp and confidence score indicating the likelihood that the entity is accurately identified. For each identified entity, a time series comprising of timestamps and corresponding confidence scores is generated and smoothed to reduce annotation noise. One or more segments containing an entity over the length of the video are obtained by detecting boundaries of the segments in the time series of the entity. From the individual temporal segmentation for each identified entity in the video, an overall temporal segmentation for the video is generated, where the overall temporal segmentation reflects the semantics of the video.
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公开(公告)号：US09627004B1
公开(公告)日：2017-04-18
申请号：US14883461
申请日：2015-10-14
Applicant: Google Inc.
Inventor： Balakrishnan Varadarajan , Sanketh Shetty , Apostol Natsev , Nitin Khandelwal , Weilong Yang , Sudheendra Vijayanarasimhan , WeiHsin Gu , Nicola Muscettola
IPC: H04N9/80 , H04N5/77 , G11B27/10 , G06K9/00 , G06K9/46 , G06F17/30 , G06F17/24 , H04N21/2743 , H04N21/234 , H04N21/235
CPC classification number: G11B27/10 , G06F17/241 , G06F17/30799 , G06F17/3082 , G06K9/00744 , G06K9/6259 , G06K9/6277 , G06K2209/27 , H04N21/23418 , H04N21/251 , H04N21/278 , H04N21/4828 , H04N21/84 , H04N21/8456
Abstract: A system and methodology provide for annotating videos with entities and associated probabilities of existence of the entities within video frames. A computer-implemented method selects an entity from a plurality of entities identifying characteristics of a video item, where the video item has associated metadata. The computer-implemented method receives probabilities of existence of the entity in video frames of the video item, and selects a video frame determined to comprise the entity responsive to determining the video frame having a probability of existence of the entity greater than zero. The computer-implemented method determines a scaling factor for the probability of existence of the entity using the metadata of the video item, and determines an adjusted probability of existence of the entity by using the scaling factor to adjust the probability of existence of the entity. The computer-implemented method labels the video frame with the adjusted probability of existence.
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公开(公告)号：US20160306804A1
公开(公告)日：2016-10-20
申请号：US15195105
申请日：2016-06-28
Applicant: Google Inc.
Inventor： Balakrishnan Varadarajan , Sudheendra Vijayanarasimhan , Sanketh Shetty , Nisarg Dilipkumar Kothari , Nicholas Delmonico Rizzolo
CPC classification number: G06F17/30551 , G06F17/30029 , G06F17/30554 , G06F17/30867 , G10L25/54 , H04L51/32 , H04L67/306
Abstract: Methods, systems, and media for presenting comments based on correlation with content are provided. In some implementations, a method for presenting ranked comments is provided, the method comprising: receiving, using a hardware processor, content data related to an item of content; receiving, using the hardware processor, comment data related to a comment associated with the item of content; determining, using the hardware processor, a degree of correlation between at least a portion of the comment data and one or more portions of the content data; determining, using the hardware processor, a priority for the comment based on the degree of correlation; and presenting, using the hardware processor, the comment based on the priority.
Abstract translation: 提供了基于与内容相关性来呈现评论的方法，系统和媒体。 在一些实现中，提供了一种用于呈现排名评论的方法，所述方法包括：使用硬件处理器接收与内容项相关的内容数据; 使用所述硬件处理器接收与所述内容项相关联的评论相关的评论数据; 使用所述硬件处理器确定所述评论数据的至少一部分与所述内容数据的一个或多个部分之间的相关程度; 基于所述相关程度来确定所述硬件处理器的所述评论的优先级; 以及基于所述优先级，使用所述硬件处理器来呈现所述注释。
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公开(公告)号：US09384242B1
公开(公告)日：2016-07-05
申请号：US13827573
申请日：2013-03-14
Applicant: Google Inc.
Inventor： Balakrishnan Varadarajan , Sudheendra Vijayanarasimhan , Sanketh Shetty , Nisarg Dilipkumar Kothari , Nicholas Delmonico Rizzolo
IPC: G06F17/30
CPC classification number: G06F17/30551 , G06F17/30029 , G06F17/30554 , G06F17/30867 , G10L25/54 , H04L51/32 , H04L67/306
Abstract: Techniques identify time-sensitive content and present the time-sensitive content to communication devices of users interested or potentially interested in the time-sensitive content. A content management component analyzes video or audio content, and extracts information from the content and determines whether the content is time-sensitive content, such as recent news-related content, based on analysis of the content and extracted information. The content management component evaluates user-related information and the extracted information, and determines whether a user(s) is likely to be interested in the time-sensitive content based on the evaluation results. The content management component sends a notification to the communication device(s) of the user(s) in response to determining the user(s) is likely to be interested in the time-sensitive content.
Abstract translation: 技术识别时间敏感内容，并向时间敏感内容感兴趣或可能感兴趣的用户的通信设备呈现时间敏感内容。 内容管理组件分析视频或音频内容，并且从内容中提取信息，并且基于对内容的分析和提取的信息来确定内容是否是时间敏感的内容，诸如最新的新闻相关内容。 内容管理组件评估用户相关信息和提取的信息，并且基于评估结果来确定用户是否可能对时间敏感内容感兴趣。 响应于确定用户可能对时间敏感内容感兴趣，内容管理组件向用户的通信设备发送通知。
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