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公开(公告)号：US20160147713A1
公开(公告)日：2016-05-26
申请号：US14548852
申请日：2014-11-20
Applicant: YAHOO! INC.
CPC classification number: G06F17/211 , G06F17/30719 , G06T3/40
Abstract: Generating notifications comprising text and image data for client devices with limited display screens is disclosed. An image to be included in the notification is resized and reshaped using image processing techniques. The resized image is further analyzed to identify optimal portions for placing the text data. The text data can also be analyzed and shortened for including at the identified portion of resized image to generate a notification. The resulting notification displays the text and image data optimally within the limited screen space of the client device so that a user observing the notification can obtain the information at a glance.
Abstract translation: 公开了包含具有有限显示屏幕的客户端设备的文本和图像数据的通知。 要包括在通知中的图像被调整大小并且使用图像处理技术重新形成。 进一步分析调整大小的图像以识别放置文本数据的最佳部分。 也可以对文本数据进行分析和缩短，以便在被调整大小的图像的识别部分包括生成通知。 所得到的通知在客户端设备的受限屏幕空间内最佳地显示文本和图像数据，以便观察该通知的用户能够一目了然地获得信息。
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公开(公告)号：US09269025B1
公开(公告)日：2016-02-23
申请号：US14609220
申请日：2015-01-29
Applicant: YAHOO! INC.
Inventor： Jen-Hao Hsiao , Jia Li
CPC classification number: G06K9/6245 , G06K9/4647 , G06K9/522 , G06K9/6212 , G06K9/629 , G06K9/64
Abstract: In an embodiment, a method comprises obtaining a frequency domain representation associated with an image; obtaining one or more frequency domain representations of one or more object detection filters; generating a composite frequency domain representation based on the frequency domain representation associated with the image and the one or more frequency domain representations of the one or more object detection filters; and detecting one or more objects in the image based on the composite frequency domain representation. The frequency domain representation associated with the image may be obtained based on a forward transform performed on an image feature description. The image feature description may be obtained based on a feature extraction performed on the image. The one or more frequency domain representations of the one or more object detection filters may be obtained based on one or more Fourier transforms performed on the one or more object detection filters.
Abstract translation: 在一个实施例中，一种方法包括获得与图像相关联的频域表示; 获得一个或多个对象检测滤波器的一个或多个频域表示; 基于与所述图像相关联的频域表示和所述一个或多个对象检测滤波器的所述一个或多个频域表示来生成复合频域表示; 以及基于所述复合频域表示来检测所述图像中的一个或多个对象。 可以基于对图像特征描述执行的正向变换来获得与图像相关联的频域表示。 可以基于对图像执行的特征提取来获得图像特征描述。 可以基于对一个或多个对象检测滤波器执行的一个或多个傅里叶变换来获得一个或多个对象检测滤波器的一个或多个频域表示。
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公开(公告)号：US20170193336A1
公开(公告)日：2017-07-06
申请号：US15463774
申请日：2017-03-20
Applicant: Yahoo! Inc.
Inventor： Suleyman Cetintas , Kuang-chih Lee , Jia Li
CPC classification number: G06K9/6267 , G06F3/04842 , G06F16/58 , G06K9/00456 , G06K9/00664 , G06K9/4628 , G06K9/6201 , G06K9/626 , G06K9/627 , G06N3/08
Abstract: Briefly, embodiments of methods and/or systems of generating preference indices for contiguous portions of digital images are disclosed. For one embodiment, as an example, parameters of a neural network may be developed to generate object labels for digital images. The developed parameters may be transferred to a neural network utilized to generate signal sample value levels corresponding to preference indices for contiguous portions of digital images.
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公开(公告)号：US20170039456A1
公开(公告)日：2017-02-09
申请号：US14820972
申请日：2015-08-07
Applicant: Yahoo! Inc.
Inventor： Mohammad Saberian , Jia Li , Jian Yang
IPC: G06K9/66
CPC classification number: G06N3/08 , G06N3/0454 , G06N3/084
Abstract: Briefly, embodiments of methods and/or systems of training multiclass convolutional neural networks (CNNs) are disclosed. For one embodiment, as an example, an auxiliary CNN may be utilized to form an ensemble with the collection as a linear combination. The linear combination may be based, at least in part, on boost prediction error encountered during the training process.
Abstract translation: 简单地，公开了训练多级卷积神经网络（CNN）的方法和/或系统的实施例。 对于一个实施例，作为示例，可以使用辅助CNN来形成集合作为线性组合的集合。 线性组合可以至少部分地基于训练过程中遇到的升压预测误差。
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15.发明申请SYSTEM AND METHOD FOR LARGE-SCALE MULTI-LABEL LEARNING USING INCOMPLETE LABEL ASSIGNMENTS 审中-公开使用不完整的标签分配进行大规模多标签学习的系统和方法公开(公告)号：US20160140451A1
公开(公告)日：2016-05-19
申请号：US14543133
申请日：2014-11-17
Applicant: YAHOO! INC.
Inventor： Jia Li , Yi Chang , Xiangnan Kong
CPC classification number: G06N99/005
Abstract: At least one label prediction model is trained, or learned, using training data that may comprise training instances that may be missing one or more labels. The at least one label prediction model may be used in identifying a content item's ground-truth label set comprising an indicator for each label in the label set indicating whether or not the label is applicable to the content item.
Abstract translation: 使用可能包含可能缺少一个或多个标签的训练实例的训练数据来训练或学习至少一个标签预测模型。 所述至少一个标签预测模型可以用于识别内容项目的地面真相标签集合，其中包括标签组中的每个标签的指示符，指示标签是否适用于内容项目。
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Patent Agency Ranking
公开(公告)号：US20150262037A1
公开(公告)日：2015-09-17
申请号：US14215925
申请日：2014-03-17
Applicant: YAHOO! INC.
Inventor： Jia Li , Xiangnan Kong
CPC classification number: G06F17/241 , G06F17/30598 , G06K9/00677 , G06K9/6296
Abstract: System, method and architecture for providing improved visual recognition by modeling visual content, semantic content and an implicit social network representing individuals depicted in a collection of content, such as visual images, photographs, etc. which network may be determined based on co-occurrences of individuals represented by the content, and/or other data linking the individuals. In accordance with one or more embodiments, using images as an example, a relationship structure may comprise an implicit structure, or network, determined from co-occurrences of individuals in the images. A kernel jointly modeling content, semantic and social network information may be built and used in automatic image annotation and/or determination of relationships between individuals, for example.
Abstract translation: 系统，方法和架构，用于通过建模视觉内容，语义内容和表示在诸如视觉图像，照片等的内容集合中描绘的个人的隐含的社交网络来提供改进的视觉识别。该网络可以基于共同事件来确定 由内容代表的个人和/或连接个人的其他数据。 根据一个或多个实施例，使用图像作为示例，关系结构可以包括由图像中的个体的共同出现确定的隐式结构或网络。 联合建模内容，语义和社交网络信息的内核可以被构建并用于例如自动图像注释和/或个体之间的关系的确定。
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公开(公告)号：US20140314313A1
公开(公告)日：2014-10-23
申请号：US13865142
申请日：2013-04-17
Applicant: YAHOO! INC.
Inventor： Lyndon Kennedy , Jia Li , Ioannis Kalantidis
CPC classification number: G06T7/0079 , G06K9/00369 , G06T7/10 , G06T7/70 , G06T2207/10024 , G06T2207/20021 , G06T2207/20076 , G06T2207/20081 , G06T2207/30196
Abstract: Techniques are provided for efficiently identifying relevant product images based on product items detected in a query image. In general, a query image may represent a digital image in any format that depicts a human body and one or more product items. For example, a query image may be an image for display on a webpage, an image captured by a user using a camera device, or an image that is part of a media content item, such as a frame from a video. Product items may be detected in a query image by segmenting the query image into a plurality of image segments and clustering one or more of the plurality image segments into one or more image segment clusters. The resulting image segments and image segment clusters may be used to search for visually similar product images.
Abstract translation: 提供了基于在查询图像中检测到的产品项目来有效地识别相关产品图像的技术。 通常，查询图像可以表示描绘人体和一个或多个产品项目的任何格式的数字图像。 例如，查询图像可以是用于在网页上显示的图像，由使用相机设备的用户捕获的图像，或作为诸如来自视频的帧的媒体内容项的一部分的图像。 可以通过将查询图像分割成多个图像片段并将多个图像片段中的一个或多个聚类成一个或多个图像片段群集来在查询图像中检测产品项目。 所得到的图像段和图像段聚类可用于搜索视觉上相似的产品图像。
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公开(公告)号：US09697442B2
公开(公告)日：2017-07-04
申请号：US14996063
申请日：2016-01-14
Applicant: YAHOO! INC.
Inventor： Jen-Hao Hsiao , Jia Li
CPC classification number: G06K9/6245 , G06K9/4647 , G06K9/522 , G06K9/6212 , G06K9/629 , G06K9/64
Abstract: In an embodiment, a method comprises obtaining a frequency domain representation associated with an image; obtaining one or more frequency domain representations of one or more object detection filters; generating a composite frequency domain representation based on the frequency domain representation associated with the image and the one or more frequency domain representations of the one or more object detection filters; and detecting one or more objects in the image based on the composite frequency domain representation. The frequency domain representation associated with the image may be obtained based on a forward transform performed on an image feature description. The image feature description may be obtained based on a feature extraction performed on the image. The one or more frequency domain representations of the one or more object detection filters may be obtained based on one or more Fourier transforms performed on the one or more object detection filters.
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公开(公告)号：US20160342626A1
公开(公告)日：2016-11-24
申请号：US15225908
申请日：2016-08-02
Applicant: Yahoo! Inc.
Inventor： Jia Li , Nadav Golbandi , XianXing Zhang
CPC classification number: G06F17/30274 , G06F17/30253 , G06F17/30256 , G06F17/30687 , G06F17/30713 , G06K9/4642 , G06K9/6223 , G06K9/6226 , G06K9/6262
Abstract: Disclosed herein is a system and method that facilitate searching and/or browsing of images by &lustering, or grouping, the images into a set of image clusters using facets, such as without limitation visual properties or visual characteristics, of the images, and representing each image cluster by a representative image selected for the image cluster. A map-reduce based probabilistic topic model may be used to identify one or more images belonging to each image cluster and update model parameters.
Abstract translation: 本文公开了一种系统和方法，其通过使用小平面（例如但不限于图像的视觉特性或视觉特征）将图像和图像分类或组合成一组图像簇来促进图像的搜索和/或浏览，并且表示每个图像 通过为图像集群选择的代表图像进行图像聚类。 可以使用基于地图缩减的概率主题模型来识别属于每个图像簇的一个或多个图像和更新模型参数。
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公开(公告)号：US20160094774A1
公开(公告)日：2016-03-31
申请号：US14500911
申请日：2014-09-29
Applicant: Yahoo! Inc.
Inventor： Jia Li , Haojian Jin
CPC classification number: H04N5/23212 , G06K9/3233 , H04N5/23216 , H04N5/23293
Abstract: An approach is provided for acquiring images with camera-enabled mobile devices using objects of interest recognition. A mobile device is configured to acquire an image represented by image data and process the image data to identify a plurality of candidate objects of interest in the image. The plurality of candidate objects of interest may be identified based upon a plurality of low level features or “cues” in the image data. Example cues include, without limitation, color contrast, edge density and superpixel straddling. A particular candidate object of interest is selected from the plurality of candidate objects of interest and a graphical symbol is displayed on a screen of the mobile device to identify the particular candidate object of interest. The particular candidate object of interest may be located anywhere on the image. Passive auto focusing is performed at the location of the particular candidate object of interest.
Abstract translation: 提供了一种用于使用感兴趣的对象识别摄像机的移动设备来获取图像的方法。 移动装置被配置为获取由图像数据表示的图像并处理图像数据以识别图像中的多个候选对象。 可以基于图像数据中的多个低级特征或“提示”来识别多个候选对象。 示例提示包括但不限于颜色对比度，边缘密度和超像素跨越。 从多个感兴趣的候选对象中选择特定的候选对象，并且在移动设备的屏幕上显示图形符号以识别特定的感兴趣的候选对象。 感兴趣的特定候选对象可以位于图像的任何地方。 在特定候选对象的位置执行被动自动对焦。
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