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

Feature selection and novelty in computational aesthetics

[Abstract] An approach for exploring novelty in expression-based evolutionary art systems is presented. The framework is composed of a feature extractor, a classifier, an evolutionary engine and a supervisor. The evolutionary engine exploits shortcomings of the classifier, generating misclassified instances. These instances update the training set and the classifier is re-trained. This iterative process forces the evolutionary algorithm to explore new paths leading to the creation of novel imagery. The experiments presented and analyzed herein explore different feature selection methods and indicate the validity of the approach.Portugal. Fundação para a Ciência e a Tecnologia; PTDC/EIA–EIA/115667/2009Galicia.Consellería de Innovación, Industria e Comercio ; PGIDIT10TIC105008P

Computational Aesthetics and Identification of Working Style

Tänapäeval kasutab meeletu hulk ettevõtteid protsessimudelitel põhinevate äriprotsesside haldamiseks, teostamiseks, monitoorimiseks ja analüüsimiseks protsessiteadlikke infosüsteeme. Lisaks genereerivad need tarkvarasüsteemid monitoorimisetapi osana ka sündmuste logisid, mis kujutavad endast tegelikku faktidest tuletatud (aposteriori) töövoogu ning neid analüüsitakse protsessiandmete hankimise tehnikate abil. Selles töös, osana protsessiandmete hankimisest, tutvustame tööstiili kontseptsiooni töö olemuse kõikehõlmava analüüsi tööriistana. Äriprotsesse ja komponentidevahelist vastastikust sõltuvust saab hinnata tööstiili perspektiivist, mis väljendub meetmetes ja mustrites. Defineerime uuendusliku sündmuste logi esitlemise lähenemise, kus logifaili käsitletakse kujutisena. Lisaks pakume välja meetmete arvutamise ja mustrite identifitseerimise algoritmid, mis põhinevad kujutiste analüüsitehnika ja arvutusesteetika kombinatsioonil. Selle tulemusena on loodud tööstiili hindamise veebipõhise rakenduse prototüüp.Nowadays, an enormous amount of companies use Process-Aware Information Systems to manage, perform, monitor and analyze business processes based on process models. Moreover, as a part of the monitoring stage, these software systems generate event logs, which represent actual a-posteriori workflow and are analyzed by process mining techniques. In this work, as a part of process mining, we introduce the concept of working style as the tool for comprehensive analysis of the nature of work. Business processes and interdependencies between its constituents can be evaluated from the perspective of working style which is represented by measures and patterns. We define the novel event log representation approach, where the log file is treated as an image. Additionally, we propose measure computation and pattern identification algorithms based on image analysis technique in combination with computational aesthetics. As a result, the web-based prototype application for working style evaluation has been built

Soft computing: forms and limits in computational aesthetics

This paper contends that soft computing can help us investigate the aesthetics of digital computation. Employing broader conceptions of aesthetics and perception, and whilst drawing upon the ontology of Alfred N. Whitehead, it uses soft computing to address the 'prehensive' dimension of the quantitative procedures of computation, and explores the interrelationship between the factuality and formality of computational structures

The pictures we like are our image: continuous mapping of favorite pictures into self-assessed and attributed personality traits

Flickr allows its users to tag the pictures they like as “favorite”. As a result, many users of the popular photo-sharing platform produce galleries of favorite pictures. This article proposes new approaches, based on Computational Aesthetics, capable to infer the personality traits of Flickr users from the galleries above. In particular, the approaches map low-level features extracted from the pictures into numerical scores corresponding to the Big-Five Traits, both self-assessed and attributed. The experiments were performed over 60,000 pictures tagged as favorite by 300 users (the PsychoFlickr Corpus). The results show that it is possible to predict beyond chance both self-assessed and attributed traits. In line with the state-of-the art of Personality Computing, these latter are predicted with higher effectiveness (correlation up to 0.68 between actual and predicted traits)

Looking Good With Flickr Faves: Gaussian Processes for Finding Difference Makers in Personality Impressions

Flickr allows its users to generate galleries of "faves", i.e., pictures that they have tagged as favourite. According to recent studies, the faves are predictive of the personality traits that people attribute to Flickr users. This article investigates the phenomenon and shows that faves allow one to predict whether a Flickr user is perceived to be above median or not with respect to each of the Big-Five Traits (accuracy up to 79\% depending on the trait). The classifier - based on Gaussian Processes with a new kernel designed for this work - allows one to identify the visual characteristics of faves that better account for the prediction outcome

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