US 12,272,069 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., New York, NY (US)
Filed on Feb. 15, 2024, as Appl. No. 18/442,361.
Application 18/442,361 is a continuation of application No. 17/360,435, filed on Jun. 28, 2021, granted, now 11,941,816.
Application 17/360,435 is a continuation of application No. 16/749,702, filed on Jan. 22, 2020, granted, now 11,080,549, issued on Aug. 3, 2021.
Prior Publication US 2024/0185426 A1, Jun. 6, 2024
This patent is subject to a terminal disclaimer.
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 a sequence of video frames by the computing device;
applying the ML predictor program to the sequence of video frames in order to generate for each respective video frame of the sequence a respective first set of runtime cropping characteristics, wherein the respective first set of runtime cropping characteristics for each respective video frame comprises first cropping coordinates for the respective video frame corresponding to a first cropped version of the respective video frame; and
storing, in non-transitory computer-readable memory, at least one respective video frame of the sequence together with the respective first set of runtime cropping characteristics for the at least one of the respective video frame,
wherein, prior to receiving the sequence of video frames, 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.