Google Inc.

WeiHsin Gu

Sudheendra Vijayanarasimhan

Nitin Khandelwal

Sanketh Shetty

Weilong Yang

Balakrishnan Varadarajan

Apostol Ivanov Natsev

Nicola Muscettola

George Dan Toderici

Tomas Izo

Min-Hsuan Tsai

Phuong B Le

Sami Ahmad Abu-El-Haija

Susanna Maria Ricco

Susana Ricco

A computer-implemented method for selecting representative frames for videos is provided. The method includes receiving a video and identifying a set of features for each of the frames of the video. The features including frame-based features and semantic features. The semantic features identifying likelihoods of semantic concepts being present as content in the frames of the video. A set of video segments for the video is subsequently generated. Each video segment includes a chronological subset of frames from the video and each frame is associated with at least one of the semantic features. The method generates a score for each frame of the subset of frames for each video segment based at least on the semantic features, and selecting a representative frame for each video segment based on the scores of the frames in the video segment. The representative frame represents and summarizes the video segment.

Selecting and presenting representative frames for video previews

A system and methodology provide for annotating videos with entities and associated probabilities of existence of the entities within video frames. A computer-implemented method selects an entity from a plurality of entities identifying characteristics of a video item, where the video item has associated metadata. The computer-implemented method receives probabilities of existence of the entity in video frames of the video item, and selects a video frame determined to comprise the entity responsive to determining the video frame having a probability of existence of the entity greater than zero. The computer-implemented method determines a scaling factor for the probability of existence of the entity using the metadata of the video item, and determines an adjusted probability of existence of the entity by using the scaling factor to adjust the probability of existence of the entity. The computer-implemented method labels the video frame with the adjusted probability of existence.

Video frame annotation

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Redwood City, CA, United States of America

WeiHsin Gu