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    UTILIZING COLOR DESCRIPTORS TO DETERMINE COLOR CONTENT OF IMAGES
    1.
    发明申请
    UTILIZING COLOR DESCRIPTORS TO DETERMINE COLOR CONTENT OF IMAGES 审中-公开
    使用颜色描述符来确定图像的颜色内容

    公开(公告)号：US20160155025A1

    公开(公告)日：2016-06-02

    申请号：US15017156

    申请日：2016-02-05

    Applicant: A9.com, Inc.

    Inventor： HIMANSHU ARORA ,  ARNAB SANAT KUMAR DHUA

    IPC: G06K9/62 ,  G06F3/0482 ,  G06T5/20 ,  G06Q30/06 ,  G06T7/40 ,  G06T7/00

    CPC classification number: G06F3/0482 ,  G06K9/4652 ,  G06K9/6218 ,  G06Q30/0643 ,  G06T5/20 ,  G06T7/11 ,  G06T7/90

    Abstract: Various embodiments provide a method for determining color information for an image. For example, a color descriptor for an image can be determined and compared against color descriptors stored for each of a number of sample images, which each represent a color in a color space. Upon comparison, matching scores can be generated for a color match between the image and each respective sample image. In this example, the number of sample images with a matching score above a threshold value can be summed and the image can be assigned to a color associated with a highest frequency of the number of sample images. Accordingly, the assigned color of the image can then be used in a “query by color” search or a browse-by-color capability.

    Abstract translation: 各种实施例提供了一种用于确定图像的颜色信息的方法。 例如，可以确定图像的颜色描述符并将其与针对每个表示颜色空间中的颜色的多个样本图像中的每一个存储的颜色描述符进行比较。 相比之下，可以为图像和每个相应样本图像之间的颜色匹配生成匹配分数。 在该示例中，可以将匹配分数高于阈值的样本图像的数量相加，并且可以将图像分配给与样本图像数量的最高频率相关联的颜色。 因此，图像的分配颜色然后可以用于“通过颜色查询”搜索或逐个浏览色彩的能力。

    TEXT ENTITY RECOGNITION
    2.
    发明申请
    TEXT ENTITY RECOGNITION 审中-公开
    文本实体识别

    公开(公告)号：US20160098611A1

    公开(公告)日：2016-04-07

    申请号：US14971318

    申请日：2015-12-16

    Applicant: A9.com, Inc.

    Inventor： DOUGLAS RYAN GRAY ,  XIAOFAN LIN ,  ARNAB SANAT KUMAR DHUA ,  YU LOU

    IPC: G06K9/32 ,  G06F17/24 ,  G06K9/00 ,  G06K9/20 ,  G06K9/62

    CPC classification number: G06K9/325 ,  G06F3/14 ,  G06F17/24 ,  G06K9/00671 ,  G06K9/2054 ,  G06K9/2072 ,  G06K9/6215 ,  G06K9/6857 ,  G06K9/723 ,  G06K2209/01

    Abstract: Various embodiments enable the identification of semi-structured text entities in an imager. The identification of the text entities is a relatively simple problem when the text is stored in a computer and free of errors, but much more challenging if the source is the output of an optical character recognition (OCR) engine from a natural scene image. Accordingly, output from an OCR engine is analyzed to isolate a character string indicative of a text entity. Each character of the string is then assigned to a character class to produce a character class string and the text entity of the string is identified based in part on a pattern of the character class string.

    Abstract translation: 各种实施例使得能够在成像器中识别半结构化文本实体。 当文本存储在计算机中并且没有错误时，文本实体的识别是相对简单的问题，但是如果源是来自自然场景图像的光学字符识别（OCR）引擎的输出，则更具挑战性。 因此，分析来自OCR引擎的输出以隔离指示文本实体的字符串。 然后将字符串的每个字符分配给字符类以产生字符类字符串，并且部分地基于字符类字符串的模式来标识字符串的文本实体。

    AUGMENTED REALITY RECOMMENDATIONS
    5.
    发明申请
    AUGMENTED REALITY RECOMMENDATIONS 审中-公开

    公开(公告)号：US20200068132A1

    公开(公告)日：2020-02-27

    申请号：US16673477

    申请日：2019-11-04

    Applicant: A9.com, Inc.

    Inventor： XIAOFAN LIN ,  ARNAB SANAT KUMAR DHUA ,  DOUGLAS RYAN GRAY ,  ATUL KUMAR ,  YU LOU

    IPC: H04N5/232 ,  G06Q30/06 ,  G06F3/00 ,  G06K9/00 ,  G06K9/34 ,  G06Q30/02 ,  G06T11/60

    Abstract: Various embodiments enable a computing device to perform tasks such as processing an image to recognize text or an object in an image to identify a particular product or related products associated with the text or object. In response to recognizing the text or the object as being associated with a product available for purchase from an electronic marketplace, one or more advertisements or product listings associated with the product can be displayed to the user. Accordingly, additional information for the associated product can be displayed, enabling the user to learn more about and purchase the product from the electronic marketplace through the portable computing device.

    SCALABLE IMAGE MATCHING
    7.
    发明申请
    SCALABLE IMAGE MATCHING 审中-公开
    可图像匹配

    公开(公告)号：US20160189000A1

    公开(公告)日：2016-06-30

    申请号：US15063050

    申请日：2016-03-07

    Applicant: A9.com, Inc.

    Inventor： SIMANT DUBE ,  SUNIL RAMESH ,  XIAOFAN LIN ,  ARNAB SANAT KUMAR DHUA ,  COLIN JON TAYLOR ,  JAISHANKER K. PILLAI

    IPC: G06K9/62

    CPC classification number: G06K9/6215 ,  G06F17/30247 ,  G06K9/00523 ,  G06K9/4676 ,  G06K9/52 ,  G06K9/6206 ,  G06K9/6211 ,  G06K9/6218 ,  G06K9/6232 ,  G06K9/6256 ,  G06K9/6267 ,  G06K9/6269 ,  G06K9/6276 ,  G06K9/6277 ,  G06K9/6284 ,  G06K9/66 ,  G06K2209/19 ,  H04N19/426 ,  H04N19/90

    Abstract: Various embodiments may increase scalability of image representations stored in a database for use in image matching and retrieval. For example, a system providing image matching can obtain images of a number of inventory items, extract features from each image using a feature extraction algorithm, and transform the same into their feature descriptor representations. These feature descriptor representations can be subsequently stored and used to compare against query images submitted by users. Though the size of each feature descriptor representation isn't particularly large, the total number of these descriptors requires a substantial amount of storage space. Accordingly, feature descriptor representations are compressed to minimize storage and, in one example, machine learning can be used to compensate for information lost as a result of the compression.

    Abstract translation: 各种实施例可以增加存储在用于图像匹配和检索的数据库中的图像表示的可扩展性。 例如，提供图像匹配的系统可以获得多个库存物品的图像，使用特征提取算法从每个图像中提取特征，并将其转换成它们的特征描述符表示。 这些特征描述符表示可随后存储并用于与用户提交的查询图像进行比较。 虽然每个特征描述符表示的大小不是特别大，但是这些描述符的总数需要大量的存储空间。 因此，压缩特征描述符表示以最小化存储，并且在一个示例中，可以使用机器学习来补偿由于压缩而丢失的信息。

    IMAGE RECOGNITION RESULT CULLING
    9.
    发明申请
    IMAGE RECOGNITION RESULT CULLING 审中-公开

    公开(公告)号：US20180060935A1

    公开(公告)日：2018-03-01

    申请号：US15792339

    申请日：2017-10-24

    Applicant: A9.com, Inc.

    Inventor： ARNAB SANAT KUMAR DHUA ,  SUNIL RAMESH

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

    CPC classification number: G06Q30/0623 ,  G06F16/532 ,  G06F16/583 ,  G06K9/4676 ,  G06K9/6202 ,  G06K9/6807 ,  G06Q30/0631

    Abstract: Various embodiments enable an image recognition system reduce the number image match candidates before running a full-fledged pair-wise match on all image match candidates. In order to accomplish this, each inventory image can be assigned to a group. For example, a title for a book sold by an electronic marketplace could be available in multiple languages, in multiple bindings, and the book could be available in print, audio book, or electronic book. Each one of these variations could be associated with its own similarly looking inventory image, each of which could be returned as a valid match to a query image for the book. Accordingly, the inventory images for these variations could be assigned to a group for the book and, instead of geometrically processing an image for each variation, the image match system can process a single image representing all of the variations.

    VISUAL SEARCH UTILIZING COLOR DESCRIPTORS
    10.
    发明申请
    VISUAL SEARCH UTILIZING COLOR DESCRIPTORS 审中-公开

    公开(公告)号：US20170277948A1

    公开(公告)日：2017-09-28

    申请号：US15618946

    申请日：2017-06-09

    Applicant: A9.com, Inc.

    Inventor： ARNAB SANAT KUMAR DHUA ,  HIMANSHU ARORA ,  SUNIL RAMESH

    IPC: G06K9/00 ,  G06K9/46

    CPC classification number: G06K9/00536 ,  G06K9/4652

    Abstract: Various embodiments provide a method for computing color descriptors of product images. For example, a number of fine color representatives can be determined to describe color variation in an image as a histogram by assigning a saturation value and a brightness value to a plurality of color hues. For each pixel of the image, the closest color among a defined fine color representative set is computed. In this example, each of the pixels is assigned a color ID corresponding to their closest matching fine color representative and at least one family color ID corresponding one or more pure color families. In this example, a histogram of the color representatives and a histogram for the color families are computed. A single color vector descriptor for the image is then determined by combining the family histogram with the color representative histogram.

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