US 11,941,885 B2
Generating a highlight video from an input video
Vadim Balannik, Rehovot (IL)
Assigned to AnyClip Ltd., Givatayim (IL)
Filed by AnyClip Ltd., Givatayim (IL)
Filed on Apr. 4, 2022, as Appl. No. 17/712,253.
Application 17/712,253 is a continuation in part of application No. 17/665,636, filed on Feb. 7, 2022, abandoned.
Application 17/665,636 is a continuation in part of application No. 17/585,679, filed on Jan. 27, 2022, granted, now 11,677,991.
Prior Publication US 2023/0260284 A1, Aug. 17, 2023
Int. Cl. G06V 20/40 (2022.01); G06V 10/74 (2022.01); G06V 10/762 (2022.01); G06V 10/774 (2022.01); G11B 27/031 (2006.01)
CPC G06V 20/47 (2022.01) [G06V 10/761 (2022.01); G06V 10/762 (2022.01); G06V 10/7747 (2022.01); G06V 20/41 (2022.01); G11B 27/031 (2013.01)] 22 Claims
OG exemplary drawing
 
1. A computer implemented method of generating at least one highlight video from an input video, comprising, using at least one processor for:
identifying a plurality of significant frames of the input video;
computing video-level features of the input video;
selecting a plurality of subsets of the plurality of significant frames;
for each subset, computing a similarity score indicating similarity between visual features of the subset and video-level features of the input video;
clustering the input video into a plurality of clusters of sequential frames according to sequential positions within the video based on the similarity scores correlated with the plurality of significant frames;
creating at least one highlight video by selecting a cluster of sequential frames of the input video;
computing a normalized score by normalizing the similarity scores of the at least one highlight video, and presenting each of the at least one highlight video a relative number of times corresponding to the normalized similarity score, wherein the at least one highlight video are presented to a plurality of client terminals accessing a web page;
testing the at least one highlight video presented the relative number of times corresponding to the normalized similarity score to obtain a click rate for each of the at least one highlight video;
generating a multi-record training dataset, wherein a record comprises a sample main video, and a ground truth of the at least one highlight video generated from the sample main video each labelled with a corresponding click rate; and
training a machine learning model for generating an outcome of a target highlight video predicted to generate a highest click rate in response to an input of a target main video.