US 11,809,486 B2
Automated image retrieval with graph neural network
Chundi Liu, Toronto (CA); Guangwei Yu, Toronto (CA); and Maksims Volkovs, Toronto (CA)
Assigned to The Toronto-Dominion Bank, Toronto (CA)
Filed by THE TORONTO-DOMINION BANK, Toronto (CA)
Filed on Aug. 31, 2022, as Appl. No. 17/900,530.
Application 17/900,530 is a continuation of application No. 16/917,422, filed on Jun. 30, 2020, granted, now 11,475,059.
Claims priority of provisional application 62/888,435, filed on Aug. 16, 2019.
Prior Publication US 2022/0414145 A1, Dec. 29, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 16/58 (2019.01); G06N 3/08 (2023.01); G06N 3/04 (2023.01)
CPC G06F 16/58 (2019.01) [G06N 3/04 (2013.01); G06N 3/08 (2013.01)] 16 Claims
OG exemplary drawing
 
1. A computer system for automated image retrieval, the computer system comprising:
a processor configured to execute instructions; and
a non-transient computer-readable medium comprising instructions that when executed by the processor cause the processor to:
receive a request from a client device to retrieve images relevant to a query image;
access a set of trained weights for a set of neighbor nodes in an image retrieval graph of a query node associated with the query image, each weight of the set of trained weights representing an edge in the image retrieval graph connecting a respective neighbor node to the query node in the image retrieval graph;
generate a query descriptor mapping the query image to a descriptor space by applying the set of trained weights to combine outputs of the set of neighbor nodes at one or more layers of the image retrieval graph;
identify relevant images based on similarity of image descriptors associated with the relevant images to the query descriptor; and
return information about the relevant images to the client device.