21
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      The Way I Paint—How Image Composition Emerges During the Creation of Abstract Artworks

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          In recent years, there has been an increasing number of studies on objective image properties in visual artworks. Little is known, however, about how these image properties emerge while artists create their artworks. In order to study this matter, I produced five colored abstract artworks by myself and recorded state images at all stages of their creation. For each image, I then calculated low-level features from deep neural networks, which served as a model of responses properties in visual cortex. Two-dimensional plots of variances that were derived from these features showed that the drawings differ greatly at early stages of their creation, but then follow a narrow common path to terminate at or close to a position where traditional paintings cluster in the plots. Whether other artists use similar perceptive strategies while they create artworks remains to be studied.

          Related collections

          Most cited references3

          • Record: found
          • Abstract: not found
          • Article: not found

          The Construction of Jackson Pollock's Fractal Drip Paintings

            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Using CNN Features to Better Understand What Makes Visual Artworks Special

            One of the goal of computational aesthetics is to understand what is special about visual artworks. By analyzing image statistics, contemporary methods in computer vision enable researchers to identify properties that distinguish artworks from other (non-art) types of images. Such knowledge will eventually allow inferences with regard to the possible neural mechanisms that underlie aesthetic perception in the human visual system. In the present study, we define measures that capture variances of features of a well-established Convolutional Neural Network (CNN), which was trained on millions of images to recognize objects. Using an image dataset that represents traditional Western, Islamic and Chinese art, as well as various types of non-art images, we show that we need only two variance measures to distinguish between the artworks and non-art images with a high classification accuracy of 93.0%. Results for the first variance measure imply that, in the artworks, the subregions of an image tend to be filled with pictorial elements, to which many diverse CNN features respond (richness of feature responses). Results for the second measure imply that this diversity is tied to a relatively large variability of the responses of individual CNN feature across the subregions of an image. We hypothesize that this combination of richness and variability of CNN feature responses is one of properties that makes traditional visual artworks special. We discuss the possible neural underpinnings of this perceptual quality of artworks and propose to study the same quality also in other types of aesthetic stimuli, such as music and literature.
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              The composition of abstract images – Differences between artists and laypersons.

                Bookmark

                Author and article information

                Journal
                Iperception
                Iperception
                IPE
                spipe
                i-Perception
                SAGE Publications (Sage UK: London, England )
                2041-6695
                9 May 2020
                May-Jun 2020
                : 11
                : 3
                : 2041669520925099
                Affiliations
                [1-2041669520925099]Experimental Aesthetics Group, Institute of Anatomy I, University of Jena School of Medicine
                Author notes
                [*]Christoph Redies, Experimental Aesthetics Group, Institute of Anatomy I, Jena University Hospital, D-07740 Jena, Germany. Email: christoph.redies@ 123456med.uni-jena.de
                Author information
                https://orcid.org/0000-0002-5220-8319
                Article
                10.1177_2041669520925099
                10.1177/2041669520925099
                7235963
                32523666
                e6cf78ca-54cf-4756-af5e-190def65aa63
                © The Author(s) 2020

                Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License ( https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

                History
                : 27 January 2020
                : 15 April 2020
                Categories
                Short and Sweet
                Custom metadata
                May-June 2020
                ts2

                Neurosciences
                experimental aesthetics,painting,creative process,image properties,low-level visual processing,deep neural network,abstract art.

                Comments

                Comment on this article