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

TL;DR

This paper investigates the features that define creativity in micro-videos, creating a dataset and proposing computational features to automatically detect creative content with promising accuracy.

Contribution

It introduces a new dataset of micro-videos labeled for creativity and develops a computational framework to automatically identify creative videos based on novelty and aesthetic value.

Findings

Features related to novelty and aesthetic value correlate with creativity

Supervised models can effectively classify creative micro-videos

Modeling both novelty and aesthetic aspects improves detection accuracy

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.