Invention Grant
- Patent Title: Image retrieval with deep local feature descriptors and attention-based keypoint descriptors
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Application No.: US15635387Application Date: 2017-06-28
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Publication No.: US10402448B2Publication Date: 2019-09-03
- Inventor: Andre Filgueiras de Araujo , Jiwoong Sim , Bohyung Han , Hyeonwoo Noh
- Applicant: Google Inc.
- Applicant Address: US CA Mountain View
- Assignee: Google LLC
- Current Assignee: Google LLC
- Current Assignee Address: US CA Mountain View
- Agency: Dority & Manning, P.A.
- Main IPC: G06K9/62
- IPC: G06K9/62 ; G06F16/583 ; G06K9/66 ; G06K9/46 ; G06K9/00

Abstract:
Systems and methods of the present disclosure can use machine-learned image descriptor models for image retrieval applications and other applications. A trained image descriptor model can be used to analyze a plurality of database images to create a large-scale index of keypoint descriptors associated with the database images. An image retrieval application can provide a query image as input to the trained image descriptor model, resulting in receipt of a set of keypoint descriptors associated with the query image. Keypoint descriptors associated with the query image can be analyzed relative to the index to determine matching descriptors (e.g., by implementing a nearest neighbor search). Matching descriptors can then be geometrically verified and used to identify one or more matching images from the plurality of database images to retrieve and provide as output (e.g., by providing for display) within the image retrieval application.
Public/Granted literature
- US20190005069A1 Image Retrieval with Deep Local Feature Descriptors and Attention-Based Keypoint Descriptors Public/Granted day:2019-01-03
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