Amazon Technologies, Inc.

Shiva Krishnamurthy

Christian Garcia Siagian

Ramakanth Mudumba

Gordon Zheng

Christian Ciabattoni

Ritesh Pase

David Niu

Lawrence Kyuil Chang

Muhammad Raffay Hamid

Hooman Mahyar

Vimal Bhat

Arjun Cholkar

Shixing Chen

Andrew James McVeigh

Steven David Prinz

Craig Critchley

Techniques for automatic scene change detection are described. As one example, a computer-implemented method includes receiving a request to train an ensemble of machine learning models on a training dataset of videos having labels that indicate scene changes to detect a scene change in a video, partitioning each video file of the training dataset of videos into a plurality of shots, training the ensemble of machine learning models into a trained ensemble of machine learning models based at least in part on the plurality of shots of the training dataset of videos and the labels that indicate scene changes, receiving an inference request for an input video, partitioning the input video into a plurality of shots, generating, by the trained ensemble of machine learning models, an inference of one or more scene changes in the input video based at least in part on the plurality of shots of the input video, and transmitting the inference to a client application or to a storage location.

Ensemble of machine learning models for automatic scene change detection

A system can be configured to receive entertainment content requested by a user and identify content segments and content features from the entertainment content. The content segments can be utilized to identify portions of the entertainment content for enrichment and/or enhancement by the system. The content features can be utilized to associate the entertainment content and the content segments with supplemental content that includes or is associated with the content features. The content features can indicate genres, scene classifications, significant figures credited with creating the entertainment content, and other points of interests for users interested in the entertainment content. The associations between the entertainment content and the supplemental content can enable the system to engage the users by presenting the supplemental content determined to match interests of the users.

Interactive entertainment content

Disclosed are various embodiments for segmenting and classifying video content using sounds. In one embodiment, a plurality of segments of a video content item are generated by analyzing audio accompanying the video content item. A subset of the plurality of segments that correspond to music segments is selected based at least in part on an audio characteristic of the subset of the plurality of segments. Individual segments of the subset of the plurality of segments are processed to determine whether a classification applies to the individual segments. A list of segments of the video content item to which the classification applies is generated.

Segmenting and classifying video content using sounds

Disclosed are various embodiments for segmenting and classifying video content using conversation. In one embodiment, a plurality of segments of a video content item are generated by analyzing audio accompanying the video content item. A subset of the plurality of segments that correspond to conversation segments are selected. Individual segments of the subset of the plurality of segments are processed to determine whether a classification applies to the individual segments. A list of segments of the video content item to which the classification applies is generated.

Segmenting and classifying video content using conversation

Growing community of inventors

Sammamish, WA, United States of America

Shiva Krishnamurthy