The notion of creativity, as opposed to related concepts such as beauty or interestingness, has not been studied from the perspective of automatic analysis of multimedia content. Meanwhile, short online videos shared on social media platforms, or micro-videos, have arisen as a new medium for creative expression. In this paper we study creative micro-videos in an effort to understand the features that make a video creative, and to address the problem of automatic detection of creative content. Defining creative videos as those that are novel and have aesthetic value, we conduct a crowdsourcing experiment to create a dataset of over 3,800 micro-videos labelled as creative and non-creative. We propose a set of computational features that we map to the components of our definition of creativity, and conduct an analysis to determine which of these features correlate most with creative video. Finally, we evaluate a supervised approach to automatically detect creative video, with promising results, showing that it is necessary to model both aesthetic value and novelty to achieve optimal classification accuracy.

6 Seconds of Sound and Vision: Creativity in Micro-videos

SCHIFANELLA, ROSSANO;
2014-01-01

Abstract

The notion of creativity, as opposed to related concepts such as beauty or interestingness, has not been studied from the perspective of automatic analysis of multimedia content. Meanwhile, short online videos shared on social media platforms, or micro-videos, have arisen as a new medium for creative expression. In this paper we study creative micro-videos in an effort to understand the features that make a video creative, and to address the problem of automatic detection of creative content. Defining creative videos as those that are novel and have aesthetic value, we conduct a crowdsourcing experiment to create a dataset of over 3,800 micro-videos labelled as creative and non-creative. We propose a set of computational features that we map to the components of our definition of creativity, and conduct an analysis to determine which of these features correlate most with creative video. Finally, we evaluate a supervised approach to automatically detect creative video, with promising results, showing that it is necessary to model both aesthetic value and novelty to achieve optimal classification accuracy.
2014
2014 IEEE Conference on Computer Vision and Pattern Recognition
Columbus, OH, USA
June 24-27
Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition
IEEE Computer Society
4272
4279
9781479951185
http://www.pamitc.org/cvpr14/
multimedia computing; computer vision; creativity
Miriam Redi;Neil OHare;Rossano Schifanella;Michele Trevisiol;Alejandro Jaimes
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/155304
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