576 research outputs found
Pandora: Description of a Painting Database for Art Movement Recognition with Baselines and Perspectives
To facilitate computer analysis of visual art, in the form of paintings, we
introduce Pandora (Paintings Dataset for Recognizing the Art movement)
database, a collection of digitized paintings labelled with respect to the
artistic movement. Noting that the set of databases available as benchmarks for
evaluation is highly reduced and most existing ones are limited in variability
and number of images, we propose a novel large scale dataset of digital
paintings. The database consists of more than 7700 images from 12 art
movements. Each genre is illustrated by a number of images varying from 250 to
nearly 1000. We investigate how local and global features and classification
systems are able to recognize the art movement. Our experimental results
suggest that accurate recognition is achievable by a combination of various
categories.To facilitate computer analysis of visual art, in the form of
paintings, we introduce Pandora (Paintings Dataset for Recognizing the Art
movement) database, a collection of digitized paintings labelled with respect
to the artistic movement. Noting that the set of databases available as
benchmarks for evaluation is highly reduced and most existing ones are limited
in variability and number of images, we propose a novel large scale dataset of
digital paintings. The database consists of more than 7700 images from 12 art
movements. Each genre is illustrated by a number of images varying from 250 to
nearly 1000. We investigate how local and global features and classification
systems are able to recognize the art movement. Our experimental results
suggest that accurate recognition is achievable by a combination of various
categories.Comment: 11 pages, 1 figure, 6 table
Synthetic Aesthetics
“Synthetic Aesthetics” is a phrase constructed on the lexical schemes of “Informational Aesthetics” and “Synthetic Phenomenology”, the research on artificial systems that possess or specify phenomenal states. It denotes the research on the cognitive capacities required for the production and the evaluation of Art through computational modelling and simulation
COMPUTATIONAL MODELLING OF HUMAN AESTHETIC PREFERENCES IN THE VISUAL DOMAIN: A BRAIN-INSPIRED APPROACH
Following the rise of neuroaesthetics as a research domain, computational aesthetics has also known a regain in popularity over the past decade with many works using novel computer vision and machine learning techniques to evaluate the aesthetic value of visual information. This thesis presents a new approach where low-level features inspired from the human visual system are extracted from images to train a machine learning-based system to classify visual information depending on its aesthetics, regardless of the type of visual media. Extensive tests are developed to highlight strengths and weaknesses of such low-level features while establishing good practices in the domain of study of computational aesthetics. The aesthetic classification system is not only tested on the most widely used dataset of photographs, called AVA, on which it is trained initially, but also on other photographic datasets to evaluate the robustness of the learnt aesthetic preferences over other rating communities.
The system is then assessed in terms of aesthetic classification on other types of visual media to investigate whether the learnt aesthetic preferences represent photography rules or more general aesthetic rules. The skill transfer from aesthetic classification of photos to videos demonstrates a satisfying correct classification rate of videos without any prior training on the test set created by Tzelepis et al. Moreover, the initial photograph classifier can also be used on feature films to investigate the classifier’s learnt visual preferences, due to films providing a large number of frames easily labellable. The study on aesthetic classification of videos concludes with a case study on the work by an online content creator. The classifier recognised a significantly greater percentage of aesthetically high frames in videos filmed in studios than on-the-go. The results obtained across datasets containing videos of diverse natures manifest the extent of the system’s aesthetic knowledge.
To conclude, the evolution of low-level visual features is studied in popular culture such as in paintings and brand logos. The work attempts to link aesthetic preferences during contemplation tasks such as aesthetic rating of photographs with preferred low-level visual features in art creation. It questions whether favoured visual features usage varies over the life of a painter, implicitly showing a relationship with artistic expertise. Findings display significant changes in use of universally preferred features over influential
vi
abstract painters’ careers such an increase in cardinal lines and the colour blue; changes that were not observed in landscape painters. Regarding brand logos, only a few features evolved in a significant manner, most of them being colour-related features.
Despite the incredible amount of data available online, phenomena developing over an entire life are still complicated to study. These computational experiments show that simple approaches focusing on the fundamentals instead of high-level measures allow to analyse artists’ visual preferences, as well as extract a community’s visual preferences from photos or videos while limiting impact from cultural and personal experiences
Ranking News-Quality Multimedia
News editors need to find the photos that best illustrate a news piece and
fulfill news-media quality standards, while being pressed to also find the most
recent photos of live events. Recently, it became common to use social-media
content in the context of news media for its unique value in terms of immediacy
and quality. Consequently, the amount of images to be considered and filtered
through is now too much to be handled by a person. To aid the news editor in
this process, we propose a framework designed to deliver high-quality,
news-press type photos to the user. The framework, composed of two parts, is
based on a ranking algorithm tuned to rank professional media highly and a
visual SPAM detection module designed to filter-out low-quality media. The core
ranking algorithm is leveraged by aesthetic, social and deep-learning semantic
features. Evaluation showed that the proposed framework is effective at finding
high-quality photos (true-positive rate) achieving a retrieval MAP of 64.5% and
a classification precision of 70%.Comment: To appear in ICMR'1
Hierarchical Image Descriptions for Classification and Painting
The overall argument this thesis makes is that topological object structures captured within hierarchical image descriptions are invariant to depictive styles and offer a level of abstraction found in many modern abstract artworks. To show how object structures can be extracted from images, two hierarchical image descriptions are proposed. The first of these is inspired by perceptual organisation; whereas, the second is based on agglomerative clustering of image primitives. This thesis argues the benefits and drawbacks of each image description and empirically show why the second is more suitable in capturing object strucutures. The value of graph theory is demonstrated in extracting object structures, especially from the second type of image description. User interaction during the structure extraction process is also made possible via an image hierarchy editor. Two applications of object structures are studied in depth. On the computer vision side, the problem of object classification is investigated. In particular, this thesis shows that it is possible to classify objects regardless of their depictive styles. This classification problem is approached using a graph theoretic paradigm; by encoding object structures as feature vectors of fixed lengths, object classification can then be treated as a clustering problem in structural feature space and that actual clustering can be done using conventional machine learning techniques. The benefits of object structures in computer graphics are demonstrated from a Non-Photorealistic Rendering (NPR) point of view. In particular, it is shown that topological object structures deliver an appropriate degree of abstraction that often appears in well-known abstract artworks. Moreover, the value of shape simplification is demonstrated in the process of making abstract art. By integrating object structures and simple geometric shapes, it is shown that artworks produced in child-like paintings and from artists such as Wassily Kandinsky, Joan Miro and Henri Matisse can be synthesised and by doing so, the current gamut of NPR styles is extended. The whole process of making abstract art is built into a single piece of software with intuitive GUI.EThOS - Electronic Theses Online ServiceGBUnited Kingdo
A model for colour naming and comparing based on conceptual neighbourhood. An application for comparing art compositions
A computational model for Qualitative Colour Description, named the QCD model, is defined using the Hue, Saturation and Luminance colour space. This model can name rainbow colours, pale, light and dark colours, and colours in the grey scale, and it has been parameterised by participants of a study in two universities in Spain: University Jaume I and University of Sevilla. The relational structure of the QCD model is analysed by means of a conceptual neighbourhood diagram and it is used to formulate a measure of similarity for solving absolute and relative comparisons of qualitative colours. Moreover, a similarity measure between colour compositions, called SimQCDI, is also developed. A survey test on several art compositions is carried out and the results obtained by the participants are analysed and compared to the computational results provided by the SimQCDI. Also, a comparison to the standard RGB Colour Histogram similarity method is carried out, which shows that the proposed similarity is more intuitive and that the results obtained are similar with respect to quantification. Finally, the cognitive adequacy of the QCD model is also analysed.This work was supported by European Commission through FP7 Marie Curie IEF actions under project COGNITIVE-AMI https://sites.google.com/site/zfalomir/projects/cognitive-ami (GA 328763), the Research Centre on Spatial Cognition at the University of Bremen, the Deutscher Akademischer Austausch Dienst (DAAD), Andalusian Regional Ministry of Economy (project SIMON TIc-8052), Spanish Ministry of Economy and Competitiveness (project TIN2011-24147), Generalitat Valenciana (project GVA/2013/135) and Universitat Jaume I (Project P11B2013-29)
- …