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    CORRELATING IMAGE ANNOTATIONS WITH FOREGROUND FEATURES
    2.
    发明申请
    CORRELATING IMAGE ANNOTATIONS WITH FOREGROUND FEATURES 有权

    公开(公告)号：US20210182333A1

    公开(公告)日：2021-06-17

    申请号：US17107483

    申请日：2020-11-30

    Applicant: eBay Inc.

    Inventor： Anurag Bhardwaj ,  Robinson Piramuthu ,  Vicente Ordonez-Roman ,  Vignesh Jagadeesh ,  Wei Di

    IPC: G06F16/58 ,  G06F16/583 ,  G06K9/46 ,  G06F16/53 ,  G06F16/50 ,  G06T7/11 ,  G06T7/10 ,  G06F16/532

    Abstract: A machine may be configured to execute a machine-learning process for identifying and understanding fine properties of various items of various types by using images and associated corresponding annotations, such as titles, captions, tags, keywords, or other textual information applied to these images. By use of a machine-learning process, the machine may perform property identification accurately and without human intervention. These item properties may be used as annotations for other images that have similar features. Accordingly, the machine may answer user-submitted questions, such as “What do rustic items look like?,” and items or images depicting items that are deemed to be rustic can be readily identified, classified, ranked, or any suitable combination thereof.

    RECOGNITION OF ITEMS DEPICTED IN IMAGES
    4.
    发明申请
    RECOGNITION OF ITEMS DEPICTED IN IMAGES 审中-公开
    识别图像中的项目

    公开(公告)号：US20160217157A1

    公开(公告)日：2016-07-28

    申请号：US14973582

    申请日：2015-12-17

    Applicant: eBay Inc.

    Inventor： Kevin Shih ,  Wei Di ,  Vignesh Jagadeesh ,  Robinson Piramuthu

    IPC: G06F17/30

    CPC classification number: G06F16/5838 ,  G06F16/5846 ,  G06F16/5854 ,  G06F16/9535

    Abstract: Products (e.g., books) often include a significant amount of informative textual information that can be used in identifying the item. An input query image is a photo (e.g., a picture taken using a mobile phone) of a product. The photo is taken from an arbitrary angle and orientation, and includes an arbitrary background (e.g., a background with significant clutter). From the query image, the identification server retrieves the corresponding clean catalog image from a database. For example, the database may be a product database having a name of the product, image of the product, price of the product, sales history for the product, or any suitable combination thereof. The retrieval is performed by both matching the image with the images in the database and matching text retrieved from the image with the text in the database.

    Abstract translation: 产品（例如书籍）通常包括可用于识别物品的大量信息性文字信息。 输入查询图像是产品的照片（例如，使用移动电话拍摄的照片）。 该照片是从任意角度和方向取得的，并且包括任意背景（例如，具有显着杂波的背景）。 从查询图像中，识别服务器从数据库检索相应的清洁目录图像。 例如，数据库可以是具有产品名称，产品图像，产品价格，产品销售历史或其任何合适组合的产品数据库。 检索是通过将图像与数据库中的图像匹配的图像和从图像中检索的匹配文本与数据库中的文本进行的。

    Estimating depth from a single image
    6.
    发明授权
    Estimating depth from a single image 有权
    从单个图像估计深度

    公开(公告)号：US09275078B2

    公开(公告)日：2016-03-01

    申请号：US14288233

    申请日：2014-05-27

    Applicant: eBay Inc.

    Inventor： Anurag Bhardwaj ,  Mohammad Haris Baig ,  Robinson Piramuthu ,  Vignesh Jagadeesh ,  Wei Di

    IPC: G06F17/30

    CPC classification number: G06T7/50 ,  G06F17/3025 ,  G06F17/30256 ,  G06F17/30262 ,  G06F17/30277 ,  G06K9/00208 ,  G06K9/4609 ,  G06T7/194 ,  G06T7/62 ,  G06T2207/10024 ,  G06T2207/10028 ,  G06T2207/20081

    Abstract: During a training phase, a machine accesses reference images with corresponding depth information. The machine calculates visual descriptors and corresponding depth descriptors from this information. The machine then generates a mapping that correlates these visual descriptors with their corresponding depth descriptors. After the training phase, the machine may perform depth estimation based on a single query image devoid of depth information. The machine may calculate one or more visual descriptors from the single query image and obtain a corresponding depth descriptor for each visual descriptor from the generated mapping. Based on obtained depth descriptors, the machine creates depth information that corresponds to the submitted single query image.

    Abstract translation: 在训练阶段，机器访问具有相应深度信息的参考图像。 该机器根据该信息计算视觉描述符和相应的深度描述符。 然后，机器生成将这些可视描述符与其相应的深度描述符相关联的映射。 在训练阶段之后，机器可以基于没有深度信息的单个查询图像来执行深度估计。 机器可以从单个查询图像计算一个或多个可视描述符，并从所生成的映射中获得每个可视描述符的相应深度描述符。 基于获得的深度描述符，机器创建与提交的单个查询图像相对应的深度信息。

    System and method for scene text recognition
    7.
    发明授权
    System and method for scene text recognition 有权
    场景文本识别的系统和方法

    公开(公告)号：US09245191B2

    公开(公告)日：2016-01-26

    申请号：US14479217

    申请日：2014-09-05

    Applicant: eBay, Inc.

    Inventor： Anurag Bhardwaj ,  Chen-Yu Lee ,  Robinson Piramuthu ,  Vignesh Jagadeesh ,  Wei Di

    IPC: G06K9/66 ,  G06K9/18 ,  G06K9/62 ,  G06K9/46

    CPC classification number: G06K9/18 ,  G06K9/3258 ,  G06K9/6231 ,  G06K9/6267 ,  G06K2009/4666 ,  G06T11/60

    Abstract: Apparatus and method for performing accurate text recognition of non-simplistic images (e.g., images with clutter backgrounds, lighting variations, font variations, non-standard perspectives, and the like) may employ a machine-learning approach to identify a discriminative feature set selected from among features computed for a plurality of irregularly positioned, sized, and/or shaped (e.g., randomly selected) image sub-regions.

    Abstract translation: 用于执行非简单图像（例如，具有杂波背景的图像，照明变化，字体变化，非标准透视等）的精确文本识别的装置和方法可以采用机器学习方法来识别所选择的区分特征集 从针对多个不规则定位，尺寸和/或成形（例如，随机选择）的图像子区域计算的特征中。

    Fashion preference analysis
    8.
    发明授权
    Fashion preference analysis 有权

    公开(公告)号：US11599929B2

    公开(公告)日：2023-03-07

    申请号：US17102194

    申请日：2020-11-23

    Applicant: eBay Inc.

    Inventor： Wei Di ,  Vignesh Jagadeesh ,  Robinson Piramuthu ,  Elizabeth Churchill ,  Anurag Bhardwaj

    IPC: G06Q30/00 ,  G06Q30/0601 ,  G06V40/10

    Abstract: A machine is configured to determine fashion preferences of users and to provide item recommendations based on the fashion preferences. For example, the machine accesses an indication of a fashion style of a user. The fashion style is determined based on automatically captured data pertaining to the user. The machine identifies, based on the fashion style, one or more fashion items from an inventory of fashion items. The machine generates one or more selectable user interface elements for inclusion in a user interface. The one or more user interface elements correspond to the one or more fashion items. The machine causes generation and display of the user interface that includes the one or more selectable user interface elements. A selection of a selectable user interface element results in display of a combination of an image of a particular fashion item and an image of an item worn by the user.

    Correlating image annotations with foreground features
    9.
    发明授权
    Correlating image annotations with foreground features 有权

    公开(公告)号：US10853407B2

    公开(公告)日：2020-12-01

    申请号：US14290754

    申请日：2014-05-29

    Applicant: eBay Inc.

    Inventor： Anurag Bhardwaj ,  Robinson Piramuthu ,  Vicente Ordonez-Roman ,  Vignesh Jagadeesh ,  Wei Di

    IPC: G06F16/58 ,  G06F16/583 ,  G06K9/46 ,  G06F16/53 ,  G06F16/50 ,  G06T7/11 ,  G06T7/10 ,  G06F16/532

    Abstract: A machine may be configured to execute a machine-learning process for identifying and understanding fine properties of various items of various types by using images and associated corresponding annotations, such as titles, captions, tags, keywords, or other textual information applied to these images. By use of a machine-learning process, the machine may perform property identification accurately and without human intervention. These item properties may be used as annotations for other images that have similar features. Accordingly, the machine may answer user-submitted questions, such as “What do rustic items look like?,” and items or images depicting items that are deemed to be rustic can be readily identified, classified, ranked, or any suitable combination thereof.

    Hierarchical deep convolutional neural network for image classification
    10.
    发明授权
    Hierarchical deep convolutional neural network for image classification 有权

    公开(公告)号：US10387773B2

    公开(公告)日：2019-08-20

    申请号：US14582059

    申请日：2014-12-23

    Applicant: eBay Inc.

    Inventor： Zhicheng Yan ,  Robinson Piramuthu ,  Vignesh Jagadeesh ,  Wei Di ,  Dennis Decoste

    IPC: G06N3/08 ,  G06N3/04

    Abstract: Hierarchical branching deep convolutional neural networks (HD-CNNs) improve existing convolutional neural network (CNN) technology. In a HD-CNN, classes that can be easily distinguished are classified in a higher layer coarse category CNN, while the most difficult classifications are done on lower layer fine category CNNs. Multinomial logistic loss and a novel temporal sparsity penalty may be used in HD-CNN training. The use of multinomial logistic loss and a temporal sparsity penalty causes each branching component to deal with distinct subsets of categories.

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