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Patent Agency Ranking
公开(公告)号：US09720934B1
公开(公告)日：2017-08-01
申请号：US14209642
申请日：2014-03-13
Applicant: A9.com, Inc.
Inventor： Simant Dube , Edward Hsiao
IPC: G06F17/30
CPC classification number: G06F17/30247 , G06K9/00805
Abstract: An object recognition system can be adapted to recognize subject matter having very few features or limited or no texture. A feature-sparse or texture-limited object can be recognized by complementing local features and/or texture features with color, region-based, shape-based, three-dimensional (3D), global, and/or composite features. Machine learning algorithms can be used to classify such objects, and image matching and verification can be adapted to the classification. Further, multiple modes of input can be integrated at various stages of the object recognition processing pipeline. These multi-modal inputs can include user feedback, additional images representing different perspectives of the object or specific regions of the object including a logo or text corresponding to the object, user behavior data, location, among others.
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公开(公告)号：US09582735B2
公开(公告)日：2017-02-28
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
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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公开(公告)号：US09280560B1
公开(公告)日：2016-03-08
申请号：US14133252
申请日：2013-12-18
Applicant: A9.com, Inc.
Inventor： Simant Dube , Sunil Ramesh , Xiaofan Lin , Arnab Sanat Kumar Dhua , Colin Jon Taylor , Jaishanker K. Pillai
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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公开(公告)号：US10650040B2
公开(公告)日：2020-05-12
申请号：US15601505
申请日：2017-05-22
Applicant: A9.com, Inc.
Inventor： Simant Dube , Edward Hsiao
IPC: G06F16/00 , G06F16/583 , G06K9/62 , G06K9/52 , G06K9/46
Abstract: An object recognition system can be adapted to recognize subject matter having very few features or limited or no texture. A feature-sparse or texture-limited object can be recognized by complementing local features and/or texture features with color, region-based, shape-based, three-dimensional (3D), global, and/or composite features. Machine learning algorithms can be used to classify such objects, and image matching and verification can be adapted to the classification. Further, multiple modes of input can be integrated at various stages of the object recognition processing pipeline. These multi-modal inputs can include user feedback, additional images representing different perspectives of the object or specific regions of the object including a logo or text corresponding to the object, user behavior data, location, among others.
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公开(公告)号：US10140549B2
公开(公告)日：2018-11-27
申请号：US15443730
申请日：2017-02-27
Applicant: A9.com, Inc.
Inventor： Simant Dube , Sunil Ramesh , Xiaofan Lin , Arnab Sanat Kumar Dhua , Colin Jon Taylor , Jaishanker K. Pillai
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.
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