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    IMAGE MATCH FOR FEATURELESS OBJECTS
    1.
    发明申请
    IMAGE MATCH FOR FEATURELESS OBJECTS 审中-公开
    用于特征对象的图像匹配

    公开(公告)号：US20160379080A1

    公开(公告)日：2016-12-29

    申请号：US15166973

    申请日：2016-05-27

    Applicant: A9.com, Inc.

    Inventor： ISMET ZEKI YALNIZ ,  COLIN JON TAYLOR ,  MEHMET NEJAT TEK ,  Shanghsuan Tsai

    IPC: G06K9/46 ,  G06T7/40 ,  G06T7/60 ,  G06T7/00

    CPC classification number: G06K9/4652 ,  G06K9/00208 ,  G06K9/4609 ,  G06K9/4676 ,  G06K9/6202 ,  G06K9/6211 ,  G06T7/11 ,  G06T7/60 ,  G06T7/90 ,  G06T2207/10004

    Abstract: Object identification through image matching can utilize ratio and other data to accurately identify objects having relatively few feature points otherwise useful for identifying objects. An initial image analysis attempts to locate a “scalar” in the image, such as may include a label, text, icon, or other identifier that can help to narrow a classification of the search, as well as to provide a frame of reference for relative measurements obtained from the image. By comparing the ratios of dimensions of the scalar with other dimensions of the object, it is possible to discriminate between objects containing that scalar in a way that is relatively robust to changes in viewpoint. A ratio signature can be generated for an object for use in matching, while in other embodiments a classification can identify priority ratios that can be used to more accurately identify objects in that classification.

    Abstract translation: 通过图像匹配的对象识别可以利用比率和其他数据来准确地识别具有相对较少的特征点的对象，否则对识别对象是有用的。 初始图像分析尝试在图像中定位“标量”，例如可以包括可以帮助缩小搜索分类的标签，文本，图标或其他标识符，以及提供用于 从图像获得的相对测量值。 通过将标量的维数与对象的其他维度进行比较，可以以对视点变化相对鲁棒的方式来区分包含该标量的对象。 可以针对用于匹配的对象生成比例签名，而在其他实施例中，分类可以标识可用于更精确地识别该分类中的对象的优先级比。

    SCALABLE IMAGE MATCHING
    4.
    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。

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