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。

公开(公告)号：US09230159B1

公开(公告)日：2016-01-05

申请号：US14100595

申请日：2013-12-09

Applicant: Google Inc.

Inventor： Sudheendra Vijayanarasimhan ,  Balakrishnan Varadarajan ,  Rahul Sukthankar

IPC: G06K9/00 ,  G06K9/62 ,  G10L15/14

CPC classification number: G06K9/00342 ,  G06K9/3241

Abstract: This disclosure generally relates to systems and methods that facilitate employing exemplar Histogram of Oriented Gradients Linear Discriminant Analysis (HOG-LDA) models along with Localizer Hidden Markov Models (HMM) to train a classification model to classify actions in videos by learning poses and transitions between the poses associated with the actions in a view of a continuous state represented by bounding boxes corresponding to where the action is located in frames of the video.

Abstract translation: 本公开通常涉及有助于使用定向梯度直方图线性判别分析（HOG-LDA）模型与定位器隐马尔可夫模型（HMM）一起使用的系统和方法来训练分类模型以通过学习姿态和视频之间的转换来分类视频中的动作 与由行动位于视频帧中的对应框所表示的连续状态的视图相关联的姿势。

公开(公告)号：US09721190B2

公开(公告)日：2017-08-01

申请号：US14933256

申请日：2015-11-05

Applicant: Google Inc.

Inventor： Sudheendra Vijayanarasimhan ,  Jay Yagnik

IPC: G06K9/62 ,  G06N3/08 ,  G06K9/66 ,  G06N3/04

CPC classification number: G06K9/6267 ,  G06K9/66 ,  G06N3/04 ,  G06N3/082

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for classification using a neural network. One of the methods for processing an input through each of multiple layers of a neural network to generate an output, wherein each of the multiple layers of the neural network includes a respective multiple nodes includes for a particular layer of the multiple layers: receiving, by a classification system, an activation vector as input for the particular layer, selecting one or more nodes in the particular layer using the activation vector and a hash table that maps numeric values to nodes in the particular layer, and processing the activation vector using the selected nodes to generate an output for the particular layer.

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

IPC: G06K9/00 ,  G06K9/52 ,  G06K9/62 ,  H04N5/265

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。