28,061 research outputs found
Computational Aesthetics for Fashion
The online fashion industry is growing fast and with it, the need for advanced systems able to automatically solve different tasks in an accurate way. With the rapid advance of digital technologies, Deep Learning has played an important role in Computational Aesthetics, an interdisciplinary area that tries to bridge fine art, design, and computer science. Specifically, Computational Aesthetics aims to automatize human aesthetic judgments with computational methods. In this thesis, we focus on three applications of computer vision in fashion, and we discuss how Computational Aesthetics helps solve them accurately
Evolving Aesthetic Maps for a Real Time Strategy Game
ArtÃculo publicado en congreso SEED'2013 (I Spanish Symposium on Entertainment Computing), Septiembre 2013, Madrid.This paper presents a procedural content generator method that have
been able to generate aesthetic maps for a real-time strategy game. The
maps has been characterized based on several of their properties in order
to de ne a similarity function between scenarios. This function has guided
a multi-objective evolution strategy during the process of generating and
evolving scenarios that are similar to other aesthetic maps while being
di erent to a set of non-aesthetic scenarios. The solutions have been
checked using a support-vector machine classi er and a self-organizing
map obtaining successful results (generated maps have been classi ed as
aesthetic maps)
6 Seconds of Sound and Vision: Creativity in Micro-Videos
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.Comment: 8 pages, 1 figures, conference IEEE CVPR 201
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