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    FINE-GRAINED CATEGORIZATION
    144.
    发明申请
    FINE-GRAINED CATEGORIZATION 有权
    精细分类

    公开(公告)号：US20160210533A1

    公开(公告)日：2016-07-21

    申请号：US14967876

    申请日：2015-12-14

    Applicant: eBay Inc

    Inventor： M. Hadi Kiapour ,  Wei Di ,  Vignesh Jagadeesh ,  Robinson Piramuthu

    IPC: G06K9/62 ,  G06K9/00 ,  G06K9/66

    CPC classification number: G06K9/6276 ,  G06K9/00979 ,  G06K9/3216 ,  G06K9/3241 ,  G06K9/4628 ,  G06K9/6229 ,  G06K9/6256 ,  G06K9/6857 ,  G06Q30/00

    Abstract: An image is passed through an image identifier to identify a coarse category for the image and a bounding box for a categorized object. A mask is used to identify the portion of the image that represents the object. Given the foreground mask, the convex hull of the mask is located and an aligned rectangle of minimum area that encloses the hull is fitted. The aligned bounding box is rotated and scaled, so that the foreground object is roughly moved to a standard orientation and size (referred to as calibrated). The calibrated image is used as an input to a fine-grained categorization module, which determines the fine category within the coarse category for the input image.

    Abstract translation: 图像通过图像标识符，以识别图像的粗略类别和分类对象的边界框。 使用掩码来识别代表对象的图像部分。 给定前景掩模，掩模的凸包被定位，并且装配包围船体的最小面积的对齐矩形。 对齐的边界框被旋转和缩放，使得前景对象粗略地移动到标准方向和大小（称为校准）。 校准图像用作细粒度分类模块的输入，该模块确定输入图像粗略类别内的精细类别。

    JOINT-BASED ITEM RECOGNITION
    145.
    发明申请
    JOINT-BASED ITEM RECOGNITION 审中-公开
    基于联合的项目识别

    公开(公告)号：US20160203525A1

    公开(公告)日：2016-07-14

    申请号：US14963026

    申请日：2015-12-08

    Applicant: eBay Inc.

    Inventor： Kota Hara ,  Vignesh Jagadeesh ,  Robinson Piramuthu

    IPC: G06Q30/02 ,  G06K9/66 ,  G06T3/40 ,  G06K9/00 ,  G06K9/62 ,  G06T11/60

    CPC classification number: G06Q30/0271 ,  G06K9/00369 ,  G06K9/6269 ,  G06K9/6277 ,  G06K9/72 ,  G06N3/0454 ,  G06N20/00 ,  G06Q30/0258 ,  G06T11/60

    Abstract: For an input image of a person, a set of object proposals are generated in the form of bounding boxes. A pose detector identifies coordinates in the image corresponding to locations on the person's body, such as the waist, head, hands, and feet of the person. A convolutional neural network receives the portions of the input image defined by the bounding boxes and generates a feature vector for each image portion. The feature vectors are input to one or more support vector machine classifiers, which generate an output representing a probability of a match with an item. The distance between the bounding box and a joint associated with the item is used to modify the probability. The modified probabilities for the support vector machine are then compared with a threshold and each other to identify the item.

    Abstract translation: 对于人的输入图像，以边界框的形式生成一组对象提议。 姿势检测器识别对应于人的身体上的位置（例如人的腰部，头部，手和脚）的图像中的坐标。 卷积神经网络接收由边界框定义的输入图像的部分，并为每个图像部分生成特征向量。 特征向量被输入到一个或多个支持向量机分类器，其产生表示与项目匹配的概率的输出。 边界框与与项目关联的关节之间的距离用于修改概率。 然后将支持向量机的修改后的概率与阈值进行比较，以确定项目。

    Complementary item recommendations using image feature data
    147.
    发明授权
    Complementary item recommendations using image feature data 有权
    使用图像特征数据的补充项目建议

    公开(公告)号：US09311666B2

    公开(公告)日：2016-04-12

    申请号：US14218772

    申请日：2014-03-18

    Applicant: eBay Inc.

    Inventor： Anurag Bhardwaj ,  Wei Di ,  Vignesh Jagadeesh ,  Robinson Piramuthu ,  Neelakantan Sundaresan

    IPC: G06F17/30 ,  G06Q30/06 ,  G06K9/46 ,  G06K9/62 ,  G06T7/40

    CPC classification number: G06K9/66 ,  G06F17/30247 ,  G06F17/3025 ,  G06F17/30256 ,  G06F17/30268 ,  G06F17/30277 ,  G06F17/3053 ,  G06F17/30867 ,  G06K9/4642 ,  G06K9/4647 ,  G06K9/4652 ,  G06K9/6215 ,  G06K9/6217 ,  G06Q30/0629 ,  G06Q30/0631 ,  G06Q30/0643 ,  G06T7/13 ,  G06T7/41 ,  G06T7/90 ,  G06T2207/10024

    Abstract: An apparatus and method to facilitate finding complementary recommendations are disclosed herein. One or more fashion trend or pleasing color combination rules are determined based on data obtained from one or more sources. One or more template images and rule triggers corresponding to the fashion trend or pleasing color combination rules are generated, each of the rule triggers associated with at least one of the template images. A processor compares a first image attribute of a particular one of the template images to a second image attribute of each of a plurality of inventory images corresponding to the plurality of inventory items to identify the inventory items complementary to the query image. The particular one of the template images is selected based on the rule trigger corresponding to the particular one of the template images being applicable for a query image.

    Abstract translation: 本文公开了一种便于查找补充建议的装置和方法。 基于从一个或多个源获得的数据来确定一个或多个时尚趋势或令人愉快的颜色组合规则。 产生与时尚趋势或令人愉悦的颜色组合规则相对应的一个或多个模板图像和规则触发器，每个规则触发器与至少一个模板图像相关联。 处理器将特定模板图像的第一图像属性与对应于多个清单项目的多个库存图像中的每一个的第二图像属性进行比较，以识别与查询图像互补的库存项目。 基于与适用于查询图像的特定模板图像相对应的规则触发来选择特定模板图像。

    COMPLEMENTARY ITEM RECOMMENDATIONS USING IMAGE FEATURE DATA
    148.
    发明申请
    COMPLEMENTARY ITEM RECOMMENDATIONS USING IMAGE FEATURE DATA 有权
    使用图像特征数据的补充项目建议

    公开(公告)号：US20140201028A1

    公开(公告)日：2014-07-17

    申请号：US14218772

    申请日：2014-03-18

    Applicant: eBay Inc.

    Inventor： Anurag Bhardwaj ,  Wei Di ,  Vignesh Jagadeesh ,  Robinson Piramuthu ,  Neelakantan Sundaresan

    IPC: G06Q30/06 ,  G06F17/30

    CPC classification number: G06K9/66 ,  G06F17/30247 ,  G06F17/3025 ,  G06F17/30256 ,  G06F17/30268 ,  G06F17/30277 ,  G06F17/3053 ,  G06F17/30867 ,  G06K9/4642 ,  G06K9/4647 ,  G06K9/4652 ,  G06K9/6215 ,  G06K9/6217 ,  G06Q30/0629 ,  G06Q30/0631 ,  G06Q30/0643 ,  G06T7/13 ,  G06T7/41 ,  G06T7/90 ,  G06T2207/10024

    Abstract: An apparatus and method to facilitate finding complementary recommendations are disclosed herein. One or more fashion trend or pleasing color combination rules are determined based on data obtained from one or more sources. One or more template images and rule triggers corresponding to the fashion trend or pleasing color combination rules are generated, each of the rule triggers associated with at least one of the template images. A processor compares a first image attribute of a particular one of the template images to a second image attribute of each of a plurality of inventory images corresponding to the plurality of inventory items to identify the inventory items complementary to the query image. The particular one of the template images is selected based on the rule trigger corresponding to the particular one of the template images being applicable for a query image.

    Abstract translation: 本文公开了一种便于查找补充建议的装置和方法。 基于从一个或多个源获得的数据来确定一个或多个时尚趋势或令人愉快的颜色组合规则。 产生与时尚趋势或令人愉悦的颜色组合规则相对应的一个或多个模板图像和规则触发器，每个规则触发器与至少一个模板图像相关联。 处理器将特定模板图像的第一图像属性与对应于多个清单项目的多个库存图像中的每一个的第二图像属性进行比较，以识别与查询图像互补的库存项目。 基于与适用于查询图像的特定模板图像相对应的规则触发来选择特定模板图像。

    METHOD, MEDIUM, AND SYSTEM FOR INTELLIGENT ONLINE PERSONAL ASSISTANT WITH IMAGE TEXT LOCALIZATION
    149.
    发明申请
    METHOD, MEDIUM, AND SYSTEM FOR INTELLIGENT ONLINE PERSONAL ASSISTANT WITH IMAGE TEXT LOCALIZATION 有权

    公开(公告)号：US20250117839A1

    公开(公告)日：2025-04-10

    申请号：US18986611

    申请日：2024-12-18

    Applicant: eBay Inc.

    Inventor： Shuai Zheng ,  Robinson Piramuthu

    IPC: G06Q30/0601 ,  G06F16/532

    Abstract: Systems, methods, and computer program products for identifying a candidate product in an electronic marketplace based on a visual comparison between candidate product image visual text content and input query image visual text content. Unlike conventional optical character recognition (OCR) based systems, embodiments automatically localize and isolate portions of a candidate product image and an input query image that each contain visual text content, and calculate a visual similarity measure between the respective portions. A trained neural network may be re-trained to more effectively find visual text content by using the localized and isolated visual text content portions as additional ground truths. The visual similarity measure serves as a visual search result score for the candidate product. Any number of images of any number of candidate products may be compared to an input query image to enable text-in-image based product searching without resorting to conventional OCR techniques.

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