Abstract
In this paper, we tackle the problem of characterizing the aesthetic appeal of consumer videos and automatically classifying them into high or low aesthetic appeal. First, we conduct a controlled user study to collect ratings on the aesthetic value of 160 consumer videos. Next, we propose and evaluate a set of low level features that are combined in a hierarchical way in order to model the aesthetic appeal of consumer videos. After selecting the 7 most discriminative features, we successfully classify aesthetically appealing vs. aesthetically unappealing videos with a 73% classification accuracy using a support vector machine.
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Moorthy, A.K., Obrador, P., Oliver, N. (2010). Towards Computational Models of the Visual Aesthetic Appeal of Consumer Videos. In: Daniilidis, K., Maragos, P., Paragios, N. (eds) Computer Vision – ECCV 2010. ECCV 2010. Lecture Notes in Computer Science, vol 6315. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15555-0_1
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DOI: https://doi.org/10.1007/978-3-642-15555-0_1
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