US 12,412,084 B2
Information processing device, information processing method, and recording medium
Ryota Fujimura, Kanagawa (JP); Yohei Nakata, Osaka (JP); and Sotaro Tsukizawa, Osaka (JP)
Assigned to PANASONIC INTELLECTUAL PROPERTY CORPORATION OF AMERICA, Torrance, CA (US)
Filed by Panasonic Intellectual Property Corporation of America, Torrance, CA (US)
Filed on Jan. 19, 2021, as Appl. No. 17/152,155.
Application 17/152,155 is a continuation of application No. PCT/JP2019/048321, filed on Dec. 10, 2019.
Claims priority of provisional application 62/787,576, filed on Jan. 2, 2019.
Claims priority of application No. 2019-107878 (JP), filed on Jun. 10, 2019.
Prior Publication US 2021/0166139 A1, Jun. 3, 2021
Int. Cl. G06N 3/08 (2023.01); G06F 18/21 (2023.01); G06F 18/2415 (2023.01); G06F 18/2431 (2023.01); G06N 5/04 (2023.01); G06N 20/00 (2019.01); G06V 10/764 (2022.01)
CPC G06N 3/08 (2013.01) [G06F 18/21 (2023.01); G06F 18/2415 (2023.01); G06F 18/2431 (2023.01); G06N 5/04 (2013.01); G06N 20/00 (2019.01); G06V 10/764 (2022.01)] 6 Claims
OG exemplary drawing
 
1. An information processing system comprising:
an information processing device including a first processor; and
a threshold calculating device including a second processor,
wherein the first processor is configured to:
obtain an image captured by a camera;
obtain a plurality of first classification thresholds outputted from the threshold calculating device, the plurality of first classification thresholds being a plurality of classification thresholds that are for classifying an object included in the image into at least one of a plurality of classes and correspond to the plurality of classes;
compare a plurality of first scalars and the plurality of first classification thresholds, wherein the plurality of first scalars are obtained by inputting the image into a first neural network that is a trained first classification model, wherein the plurality of first scalars correspond to the plurality of classes, wherein the plurality of first scalars are a plurality of scalars that are not transformed into classification probability values, and wherein the plurality of first classification thresholds correspond to the plurality of classes;
classify the object into, among the plurality of classes, at least one class having a corresponding first scalar that is greater than a corresponding first classification threshold; and
output, as a classification result, an information relating to the at least one class into which the object is classified,
wherein the second processor is configured to:
obtain a set of images for training that includes a plurality of images for training and a plurality of correct data items corresponding to the plurality of images for training;
train a second neural network using the image set for training, to output, from each of the plurality of images for training inputted, the plurality of first scalars corresponding to the plurality of classes, the second neural network being a classification model;
perform a first transform that transforms a plurality of second scalars into a plurality of classification probability values, the plurality of second scalars corresponding to the plurality of classes and being obtained by inputting an image for evaluation into the second neural network trained;
determine a plurality of second classification thresholds based on the plurality of classification probability values;
perform a second transform that is a transform from the plurality of second classification thresholds into the plurality of first classification thresholds and an inverse transform of the first transform; and
output the plurality of first classification thresholds into which the plurality of second classification thresholds have been transformed by the second transform to the information processing device.