Diversity and stylization of the contemporary user-generated visual arts in the complexity-entropy plane

• URL: http://arxiv.org/abs/2408.10356v2
• Date: Wed, 21 Aug 2024 16:42:06 GMT
• Title: Diversity and stylization of the contemporary user-generated visual arts in the complexity-entropy plane
• Authors: Seunghwan Kim, Byunghwee Lee, Wonjae Lee,
• Abstract summary: We investigate an evolutionary process underpinning the emergence and stylization of visual art styles using the complexity-entropy (C-H) plane. We analyze 149,780 images curated in DeviantArt and Behance platforms from 2010 to 2020. Results reveal significant statistical relationships between the C-H information of visual artistic styles and the dissimilarities of the multi-level image features.
• Score: 3.6241617325524853
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The advent of computational and numerical methods in recent times has provided new avenues for analyzing art historiographical narratives and tracing the evolution of art styles therein. Here, we investigate an evolutionary process underpinning the emergence and stylization of contemporary user-generated visual art styles using the complexity-entropy (C-H) plane, which quantifies local structures in paintings. Informatizing 149,780 images curated in DeviantArt and Behance platforms from 2010 to 2020, we analyze the relationship between local information of the C-H space and multi-level image features generated by a deep neural network and a feature extraction algorithm. The results reveal significant statistical relationships between the C-H information of visual artistic styles and the dissimilarities of the multi-level image features over time within groups of artworks. By disclosing a particular C-H region where the diversity of image representations is noticeably manifested, our analyses reveal an empirical condition of emerging styles that are both novel in the C-H plane and characterized by greater stylistic diversity. Our research shows that visual art analyses combined with physics-inspired methodologies and machine learning, can provide macroscopic insights into quantitatively mapping relevant characteristics of an evolutionary process underpinning the creative stylization of uncharted visual arts of given groups and time.

Related papers

• IDArb: Intrinsic Decomposition for Arbitrary Number of Input Views and Illuminations [64.07859467542664]
  Capturing geometric and material information from images remains a fundamental challenge in computer vision and graphics. Traditional optimization-based methods often require hours of computational time to reconstruct geometry, material properties, and environmental lighting from dense multi-view inputs. We introduce IDArb, a diffusion-based model designed to perform intrinsic decomposition on an arbitrary number of images under varying illuminations.
  arXiv  Detail & Related papers  (2024-12-16T18:52:56Z)
• Convolution goes higher-order: a biologically inspired mechanism empowers image classification [0.8999666725996975]
  We propose a novel approach to image classification inspired by complex nonlinear biological visual processing. Our model incorporates a Volterra-like expansion of the convolution operator, capturing multiplicative interactions. Our work bridges neuroscience and deep learning, offering a path towards more effective, biologically inspired computer vision models.
  arXiv  Detail & Related papers  (2024-12-09T18:33:09Z)
• Style-based Clustering of Visual Artworks and the Play of Neural Style-Representations [2.4374097382908477]
  Clustering artworks based on style can have many potential real-world applications like art recommendations, style-based search and retrieval. We argue that clustering artworks based on style is largely an unaddressed problem.
  arXiv  Detail & Related papers  (2024-09-12T17:44:07Z)
• Diffusion-Based Visual Art Creation: A Survey and New Perspectives [51.522935314070416]
  This survey explores the emerging realm of diffusion-based visual art creation, examining its development from both artistic and technical perspectives. Our findings reveal how artistic requirements are transformed into technical challenges and highlight the design and application of diffusion-based methods within visual art creation. We aim to shed light on the mechanisms through which AI systems emulate and possibly, enhance human capacities in artistic perception and creativity.
  arXiv  Detail & Related papers  (2024-08-22T04:49:50Z)
• GalleryGPT: Analyzing Paintings with Large Multimodal Models [64.98398357569765]
  Artwork analysis is important and fundamental skill for art appreciation, which could enrich personal aesthetic sensibility and facilitate the critical thinking ability. Previous works for automatically analyzing artworks mainly focus on classification, retrieval, and other simple tasks, which is far from the goal of AI. We introduce a superior large multimodal model for painting analysis composing, dubbed GalleryGPT, which is slightly modified and fine-tuned based on LLaVA architecture.
  arXiv  Detail & Related papers  (2024-08-01T11:52:56Z)
• Controllable Mind Visual Diffusion Model [58.83896307930354]
  Brain signal visualization has emerged as an active research area, serving as a critical interface between the human visual system and computer vision models. We propose a novel approach, referred to as Controllable Mind Visual Model Diffusion (CMVDM) CMVDM extracts semantic and silhouette information from fMRI data using attribute alignment and assistant networks. We then leverage a control model to fully exploit the extracted information for image synthesis, resulting in generated images that closely resemble the visual stimuli in terms of semantics and silhouette.
  arXiv  Detail & Related papers  (2023-05-17T11:36:40Z)
• A domain adaptive deep learning solution for scanpath prediction of paintings [66.46953851227454]
  This paper focuses on the eye-movement analysis of viewers during the visual experience of a certain number of paintings. We introduce a new approach to predicting human visual attention, which impacts several cognitive functions for humans. The proposed new architecture ingests images and returns scanpaths, a sequence of points featuring a high likelihood of catching viewers' attention.
  arXiv  Detail & Related papers  (2022-09-22T22:27:08Z)
• Compression ensembles quantify aesthetic complexity and the evolution of visual art [0.0]
  We generalize and extend the compression approach to quantify algorithmic distance in historical and contemporary visual media. The proposed "ensemble" approach works by compressing a large number of transformed versions of a given input image. We show how the approach can be used to reveal and quantify trends in art historical data, both on the scale of centuries and in rapidly evolving contemporary NFT art markets.
  arXiv  Detail & Related papers  (2022-05-20T16:05:22Z)
• Learning Portrait Style Representations [34.59633886057044]
  We study style representations learned by neural network architectures incorporating higher level characteristics. We find variation in learned style features from incorporating triplets annotated by art historians as supervision for style similarity. We also present the first large-scale dataset of portraits prepared for computational analysis.
  arXiv  Detail & Related papers  (2020-12-08T01:36:45Z)
• Art Style Classification with Self-Trained Ensemble of AutoEncoding Transformations [5.835728107167379]
  Artistic style of a painting is a rich descriptor that reveals both visual and deep intrinsic knowledge about how an artist uniquely portrays and expresses their creative vision. In this paper, we investigate the use of deep self-supervised learning methods to solve the problem of recognizing complex artistic styles with high intra-class and low inter-class variation.
  arXiv  Detail & Related papers  (2020-12-06T21:05:23Z)
• Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation [84.7571086566595]
  We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape. The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
  arXiv  Detail & Related papers  (2020-03-31T11:50:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.