Estimating Beauty Ratings of Videos using Supervoxels

The major low-level perceptual components that influence the beauty ratings of video are color, contrast, and motion. To estimate the beauty ratings of the NHK dataset, we propose to extract these features based on supervoxels, which are a group of pixels that share similar color and spatial information through the temporal domain. Recent beauty methods use frame-level processing for visual features and disregard the spatio-temporal aspect of beauty. In this paper, we explicitly model this property by introducing supervoxel-based visual and motion features. In order to create a beauty estimator, we first identify 60 videos (either beautiful or not beautiful) in the NHK dataset. We then train a neural network regressor using the supervoxel-based features and binary beauty ratings. We rate the 1000 videos in the NHK dataset and rank them according to their ratings. When comparing our rankings with the actual rankings of the NHK dataset, we obtain a Spearman correlation coefficient of 0.42.

Published in:
Proceedings of the 21st ACM international conference on Multimedia, 385-388
Presented at:
21st ACM International Conference on Multimedia, Barcelona, Spain, October 21-25, 2013

Note: The status of this file is: Anyone

 Record created 2013-11-01, last modified 2020-10-28

Download fulltext

Rate this document:

Rate this document:
(Not yet reviewed)