1,621 research outputs found
A method for extracting emotion using colors comprise the painting image
Paintings can evoke emotions in viewers. In this paper, we propose a method for extracting emotions from paintings by using the colors that comprise the paintings. The proposed approach is based on a color image scale, which is one of the popular experimental scales focusing on the relation between colors and emotions. We first construct a color combination and emotional word dataset. To this end, we create a color spectrum from the input painting. We then search for the best matching color combination from the dataset, which is most similar to the color spectrum. The best matching color combination is mapped to the corresponding emotional word. Afterward, we extract the emotional word as the emotion evoked by the painting. To evaluate the proposed method, we compared the results of the proposed algorithm to those of a user study on the extraction of emotions from several paintings. Through several experiments, we show that the proposed method exhibits excellent performance with respect to predicting the emotions evoked by a painting. Finally, we propose an image exploration system based on the emotion extraction method mentioned above. In this system, users can explore painting images emotionally coherently
Concrete Painting
This thesis explores the evolution of my artwork with synthetic materials through influences of the Minimalist and Process Artists of the 1960\u27s and 1970\u27s, inspiration from natural forms, and my exploration of concrete painting. Each work reveals discoveries of different processes and materials, accelerating the creation of new works. It is my hope this thesis will inform viewers about the process and concepts that my work embodies
Artificial Intelligence in the Creative Industries: A Review
This paper reviews the current state of the art in Artificial Intelligence
(AI) technologies and applications in the context of the creative industries. A
brief background of AI, and specifically Machine Learning (ML) algorithms, is
provided including Convolutional Neural Network (CNNs), Generative Adversarial
Networks (GANs), Recurrent Neural Networks (RNNs) and Deep Reinforcement
Learning (DRL). We categorise creative applications into five groups related to
how AI technologies are used: i) content creation, ii) information analysis,
iii) content enhancement and post production workflows, iv) information
extraction and enhancement, and v) data compression. We critically examine the
successes and limitations of this rapidly advancing technology in each of these
areas. We further differentiate between the use of AI as a creative tool and
its potential as a creator in its own right. We foresee that, in the near
future, machine learning-based AI will be adopted widely as a tool or
collaborative assistant for creativity. In contrast, we observe that the
successes of machine learning in domains with fewer constraints, where AI is
the `creator', remain modest. The potential of AI (or its developers) to win
awards for its original creations in competition with human creatives is also
limited, based on contemporary technologies. We therefore conclude that, in the
context of creative industries, maximum benefit from AI will be derived where
its focus is human centric -- where it is designed to augment, rather than
replace, human creativity
Text–to–Video: Image Semantics and NLP
When aiming at automatically translating an arbitrary text into a visual story, the main challenge consists in finding a semantically close visual representation whereby the displayed meaning should remain the same as in the given text. Besides, the appearance of an image itself largely influences how its meaningful information is transported towards an observer. This thesis now demonstrates that investigating in both, image semantics as well as the semantic relatedness between visual and textual sources enables us to tackle the challenging semantic gap and to find a semantically close translation from natural language to a corresponding visual representation.
Within the last years, social networking became of high interest leading to an enormous and still increasing amount of online available data. Photo sharing sites like Flickr allow users to associate textual information with their uploaded imagery. Thus, this thesis exploits this huge knowledge source of user generated data providing initial links between images and words, and other meaningful data.
In order to approach visual semantics, this work presents various methods to analyze the visual structure as well as the appearance of images in terms of meaningful similarities, aesthetic appeal, and emotional effect towards an observer. In detail, our GPU-based approach efficiently finds visual similarities between images in large datasets across visual domains and identifies various meanings for ambiguous words exploring similarity in online search results. Further, we investigate in the highly subjective aesthetic appeal of images and make use of deep learning to directly learn aesthetic rankings from a broad diversity of user reactions in social online behavior. To gain even deeper insights into the influence of visual appearance towards an observer, we explore how simple image processing is capable of actually changing the emotional perception and derive a simple but effective image filter.
To identify meaningful connections between written text and visual representations, we employ methods from Natural Language Processing (NLP). Extensive textual processing allows us to create semantically relevant illustrations for simple text elements as well as complete storylines. More precisely, we present an approach that resolves dependencies in textual descriptions to arrange 3D models correctly. Further, we develop a method that finds semantically relevant illustrations to texts of different types based on a novel hierarchical querying algorithm. Finally, we present an optimization based framework that is capable of not only generating semantically relevant but also visually coherent picture stories in different styles.Bei der automatischen Umwandlung eines beliebigen Textes in eine visuelle Geschichte, besteht die größte Herausforderung darin eine semantisch passende visuelle Darstellung zu finden. Dabei sollte die Bedeutung der Darstellung dem vorgegebenen Text entsprechen. Darüber hinaus hat die Erscheinung eines Bildes einen großen Einfluß darauf, wie seine bedeutungsvollen Inhalte auf einen Betrachter übertragen werden. Diese Dissertation zeigt, dass die Erforschung sowohl der Bildsemantik als auch der semantischen Verbindung zwischen visuellen und textuellen Quellen es ermöglicht, die anspruchsvolle semantische Lücke zu schließen und eine semantisch nahe Übersetzung von natürlicher Sprache in eine entsprechend sinngemäße visuelle Darstellung zu finden.
Des Weiteren gewann die soziale Vernetzung in den letzten Jahren zunehmend an Bedeutung, was zu einer enormen und immer noch wachsenden Menge an online verfügbaren Daten geführt hat. Foto-Sharing-Websites wie Flickr ermöglichen es Benutzern, Textinformationen mit ihren hochgeladenen Bildern zu verknüpfen. Die vorliegende Arbeit nutzt die enorme Wissensquelle von benutzergenerierten Daten welche erste Verbindungen zwischen Bildern und Wörtern sowie anderen aussagekräftigen Daten zur Verfügung stellt.
Zur Erforschung der visuellen Semantik stellt diese Arbeit unterschiedliche Methoden vor, um die visuelle Struktur sowie die Wirkung von Bildern in Bezug auf bedeutungsvolle Ähnlichkeiten, ästhetische Erscheinung und emotionalem Einfluss auf einen Beobachter zu analysieren. Genauer gesagt, findet unser GPU-basierter Ansatz effizient visuelle Ähnlichkeiten zwischen Bildern in großen Datenmengen quer über visuelle Domänen hinweg und identifiziert verschiedene Bedeutungen für mehrdeutige Wörter durch die Erforschung von Ähnlichkeiten in Online-Suchergebnissen. Des Weiteren wird die höchst subjektive ästhetische Anziehungskraft von Bildern untersucht und "deep learning" genutzt, um direkt ästhetische Einordnungen aus einer breiten Vielfalt von Benutzerreaktionen im sozialen Online-Verhalten zu lernen. Um noch tiefere Erkenntnisse über den Einfluss des visuellen Erscheinungsbildes auf einen Betrachter zu gewinnen, wird erforscht, wie alleinig einfache Bildverarbeitung in der Lage ist, tatsächlich die emotionale Wahrnehmung zu verändern und ein einfacher aber wirkungsvoller Bildfilter davon abgeleitet werden kann.
Um bedeutungserhaltende Verbindungen zwischen geschriebenem Text und visueller Darstellung zu ermitteln, werden Methoden des "Natural Language Processing (NLP)" verwendet, die der Verarbeitung natürlicher Sprache dienen. Der Einsatz umfangreicher Textverarbeitung ermöglicht es, semantisch relevante Illustrationen für einfache Textteile sowie für komplette Handlungsstränge zu erzeugen. Im Detail wird ein Ansatz vorgestellt, der Abhängigkeiten in Textbeschreibungen auflöst, um 3D-Modelle korrekt anzuordnen. Des Weiteren wird eine Methode entwickelt die, basierend auf einem neuen hierarchischen Such-Anfrage Algorithmus, semantisch relevante Illustrationen zu Texten verschiedener Art findet. Schließlich wird ein optimierungsbasiertes Framework vorgestellt, das nicht nur semantisch relevante, sondern auch visuell kohärente Bildgeschichten in verschiedenen Bildstilen erzeugen kann
Fine Art Pattern Extraction and Recognition
This is a reprint of articles from the Special Issue published online in the open access journal Journal of Imaging (ISSN 2313-433X) (available at: https://www.mdpi.com/journal/jimaging/special issues/faper2020)
A Data Set and a Convolutional Model for Iconography Classification in Paintings
Iconography in art is the discipline that studies the visual content of
artworks to determine their motifs and themes andto characterize the way these
are represented. It is a subject of active research for a variety of purposes,
including the interpretation of meaning, the investigation of the origin and
diffusion in time and space of representations, and the study of influences
across artists and art works. With the proliferation of digital archives of art
images, the possibility arises of applying Computer Vision techniques to the
analysis of art images at an unprecedented scale, which may support iconography
research and education. In this paper we introduce a novel paintings data set
for iconography classification and present the quantitativeand qualitative
results of applying a Convolutional Neural Network (CNN) classifier to the
recognition of the iconography of artworks. The proposed classifier achieves
good performances (71.17% Precision, 70.89% Recall, 70.25% F1-Score and 72.73%
Average Precision) in the task of identifying saints in Christian religious
paintings, a task made difficult by the presence of classes with very similar
visual features. Qualitative analysis of the results shows that the CNN focuses
on the traditional iconic motifs that characterize the representation of each
saint and exploits such hints to attain correct identification. The ultimate
goal of our work is to enable the automatic extraction, decomposition, and
comparison of iconography elements to support iconographic studies and
automatic art work annotation.Comment: Published at ACM Journal on Computing and Cultural Heritage (JOCCH)
https://doi.org/10.1145/345888
The display makes a difference: A mobile eye tracking study on the perception of art before and after a museum’s rearrangement
There is increasing awareness that the perception of art is affected by the way it is presented. In 2018, the Austrian Gallery Belvedere redisplayed its permanent collection. Our multi-disciplinary team seized this opportunity to investigate the viewing behavior of specific artworks both before and after the museum’s rearrangement. In contrast to previous mobile eye tracking (MET) studies in museums, this study benefits from the comparison of two realistic display conditions (without any research interference), an unconstrained study design (working with regular museum visitors), and a large data sample (comprising 259 participants). We employed a mixed-method approach that combined mobile eye tracking, subjective mapping (a drawing task in conjunction with an open interview), and a questionnaire in order to relate gaze patterns to processes of meaning-making. Our results show that the new display made a difference in that it 1) generally increased the viewing times of the artworks; 2) clearly extended the reading times of labels; and 3) deepened visitors’ engagement with the artworks in their exhibition reflections. In contrast, interest in specific artworks and art form preferences proved to be robust and independent of presentation modes
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