000190097 001__ 190097
000190097 005__ 20190316235738.0
000190097 020__ $$a978-1-4503-2404-5
000190097 0247_ $$2doi$$a10.1145/2502081.2508125
000190097 037__ $$aCONF
000190097 245__ $$aEstimating Beauty Ratings of Videos using Supervoxels
000190097 269__ $$a2013
000190097 260__ $$c2013
000190097 336__ $$aConference Papers
000190097 520__ $$aThe major low-level perceptual components that influence the beauty ratings of video are color, contrast, and motion. To estimate the beauty ratings of the NHK dataset, we propose to extract these features based on supervoxels, which are a group of pixels that share similar color and spatial information through the temporal domain. Recent beauty methods use frame-level processing for visual features and disregard the spatio-temporal aspect of beauty. In this paper, we explicitly model this property by introducing supervoxel-based visual and motion features. In order to create a beauty estimator, we first identify 60 videos (either beautiful or not beautiful) in the NHK dataset. We then train a neural network regressor using the supervoxel-based features and binary beauty ratings. We rate the 1000 videos in the NHK dataset and rank them according to their ratings. When comparing our rankings with the actual rankings of the NHK dataset, we obtain a Spearman correlation coefficient of 0.42.
000190097 6531_ $$aVideo beauty
000190097 6531_ $$asupervoxel
000190097 6531_ $$avideo ranking
000190097 700__ $$0245739$$aYildirim, Gökhan$$g200257
000190097 700__ $$0245251$$aShaji, Appu$$g188751
000190097 700__ $$0241946$$aSüsstrunk, Sabine$$g125681
000190097 7112_ $$a21st ACM International Conference on Multimedia$$cBarcelona, Spain$$dOctober 21-25, 2013
000190097 773__ $$q385-388$$tProceedings of the 21st ACM international conference on Multimedia
000190097 8564_ $$s598216$$uhttps://infoscience.epfl.ch/record/190097/files/MM213gc_yildirim.pdf$$yn/a$$zn/a
000190097 909C0 $$0252320$$pIVRL$$xU10429
000190097 909CO $$ooai:infoscience.tind.io:190097$$pconf$$pIC$$qGLOBAL_SET
000190097 917Z8 $$x200257
000190097 937__ $$aEPFL-CONF-190097
000190097 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000190097 980__ $$aCONF