US 11,941,816 B2
Automated cropping of images using a machine learning predictor
Aneesh Vartakavi, Emeryville, CA (US); and Casper Lützhøft Christensen, Emeryville, CA (US)
Assigned to Gracenote, Inc., New York, NY (US)
Filed by Gracenote, Inc., Emeryville, CA (US)
Filed on Jun. 28, 2021, as Appl. No. 17/360,435.
Application 17/360,435 is a continuation in part of application No. 16/749,702, filed on Jan. 22, 2020, granted, now 11,080,549.
Prior Publication US 2021/0327071 A1, Oct. 21, 2021
Int. Cl. G06T 7/11 (2017.01); G06N 3/08 (2023.01); G06T 7/174 (2017.01); G06V 10/25 (2022.01); G06V 10/26 (2022.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01)
CPC G06T 7/11 (2017.01) [G06N 3/08 (2013.01); G06T 7/174 (2017.01); G06V 10/25 (2022.01); G06V 10/267 (2022.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20132 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method carried out by a machine learning (ML) predictor program implemented on a computing device and configured for generating predicted cropping characteristics for input images, wherein cropping characteristics for any given input image comprise coordinates of cropping boundaries with respect to the any given input image prior to cropping, the method comprising:
receiving one or more uncropped images by the computing device;
applying the ML predictor program to the one or more uncropped images in order to generate for each respective uncropped image of the one or more uncropped images a respective set of runtime cropping characteristics, wherein the respective set of runtime cropping characteristics for each respective uncropped image comprises one or more subsets of cropping coordinates for the respective uncropped image, and wherein each subset corresponds to a different cropped version of the respective uncropped image; and
storing, in non-transitory computer-readable memory, the one or more uncropped images together with the respective set of runtime cropping characteristics for each respective uncropped image of the one or more uncropped images,
wherein, prior to receiving the one or more uncropped images, the ML predictor program has been trained to predict cropping characteristics for each respective training raw image of a plurality of training raw images, based on expected cropping characteristics represented in a respective set of training master images associated with the respective training raw image,
and wherein each training master image of the respective set of training master images indicates respective cropping characteristics defined for the associated respective training raw image.