Other

Carnegie Mellon University

Michael Jay Tarr

Mark Desnoyer

Deborah Jeanne Johnson

Sophie Lebrecht

Sunil Mallya Kasaragod

Padraig Michael Furlong

Sunil Mallya

Nicholas Paul Dufour

David Henry Lea

In one embodiment, a plurality of images is received. The plurality of images are frames of a video file. A user requests for a thumbnail picture representative of the plurality of images. The plurality of images are filtered to obtain a set of images. The filtering can be based on a blurriness of the image, whether an image is near a scene transition, an amount of text depicted in the image, or a color level of the image. Valence scores may be determined for one or more of the images in the set of images. Valence scores are based on determining values of characteristics of an image that can predict user responses to the image. A first image from the set of images is selected based at least in part on the valence score of the first image. The first image is sent for display.

Selecting a high valence representative image

Access is provided to optimal thumbnails that are extracted from a stream of video. Using a processing device configured with a model that incorporates preferences generated by the brain and behavior from the perception of visual images, the optimal thumbnail(s) for a given video is/are selected, stored and/or displayed.

Automated thumbnail selection for online video

Growing community of inventors

Pittsburgh, PA, United States of America

Michael Jay Tarr