首页/论文/2002.05107

arXiv 2002.051072020-02cs.CV

Analysis of Dutch Master Paintings with Convolutional Neural Networks

Steven J. Frank, Andrea M. Frank

英文摘要：

Trained on the works of an artist under study and visually comparable works of other artists, convolutional neural networks can identify forgeries and provide attributions. They can also assign classification probabilities within a painting, revealing mixed authorship and identifying regions painted by different hands.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

阅读原文

阅读 PDF ↗

arXiv 页面 ↗HTML 原文 ↗

本文内容

英文摘要相关论文

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

继续探索

相关论文

PATCH：一种评估历史画作中艺术实践异质性的深度学习方法

PATCH: a deep learning method to assess heterogeneity of artistic practice in historical paintings

Case Western Reserve University（凯斯西储大学）；Department of Physics（物理系）；Department of Art History and Art（艺术史与艺术系）；The Frick Pittsburgh Museums and Gardens（弗里克匹兹堡博物馆和花园）；Department of Computer and Data Sciences（计算机与数据科学系）；MORE Center（MORE中心）；Cleveland Institute of Art（克利夫兰艺术学院）；Factum Foundation（Factum基金会）

提出名为PATCH的新型机器学习方法，在无外部训练数据下识别历史画作中的个体艺术实践模式，并应用于埃尔·格雷科画作以量化艺术实践的异质性。

2025-07-16cs.AIcs.CVcs.LG

师徒关系中艺术品的颠覆性转变：以浮世绘艺术品为例

Disruptive Transformation of Artworks in Master-Disciple Relationships: The Case of Ukiyo-e Artworks

Japan Advanced Institute of Science and Technology, Co-creative Intelligence Research Area（日本科学技术先进研究院，协同创造力研究所）；Research Institute for Economics & Business Administration, Kobe University（经济与商业管理研究所，兵库大学）

本文以11,000张浮世绘高分辨率图像为对象，采用基于网络的创造性计算方法，定量分析作品与艺术家的创造性，揭示其整体外观创造性下降而风格更细分且保持高创造性的演变规律。

2025-05-13cs.CVcs.LG

Extracting associations and meanings of objects depicted in artworks through bi-modal deep networks

King’s College London（伦敦国王学院）；University of Cambridge（剑桥大学）；Oxia Palus

We present a novel bi-modal system based on deep networks to address the problem of learning associations and simple meanings of objects depicted in "authored" images, such as fine art paintings and drawings. Our overall system processes both the images and associated texts in order to learn associations between images of individual objects, their identities and the abstract meanings they signify. Unlike past deep nets that describe depicted objects and infer predicates, our system identifies meaning-bearing objects ("signifiers") and their associations ("signifieds") as well as basic overall …

2022-03-16cs.CLcs.CVcs.LG

Toward Automated Discovery of Artistic Influence

Rutgers, The State University of New Jersey（罗格斯，新泽西州立大学）

Considering the huge amount of art pieces that exist, there is valuable information to be discovered. Examining a painting, an expert can determine its style, genre, and the time period that the painting belongs. One important task for art historians is to find influences and connections between artists. Is influence a task that a computer can measure? The contribution of this paper is in exploring the problem of computer-automated suggestion of influences between artists, a problem that was not addressed before in a general setting. We first present a comparative study of different classifica…

2014-08-14cs.CVcs.LG

Learning to Sketch with Deep Q Networks and Demonstrated Strokes

University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Adobe Research（奥多比研究院）

Doodling is a useful and common intelligent skill that people can learn and master. In this work, we propose a two-stage learning framework to teach a machine to doodle in a simulated painting environment via Stroke Demonstration and deep Q-learning (SDQ). The developed system, Doodle-SDQ, generates a sequence of pen actions to reproduce a reference drawing and mimics the behavior of human painters. In the first stage, it learns to draw simple strokes by imitating in supervised fashion from a set of strokeaction pairs collected from artist paintings. In the second stage, it is challenged to dr…

2018-10-14cs.CV

使用半监督回归深度学习模型进行古画平纹织物的线数计数

Thread Counting in Plain Weave for Old Paintings Using Semi-Supervised Regression Deep Learning Models

Universidad de Sevilla（塞维利亚大学）；Museo Nacional del Prado（普拉多国家博物馆）

本文提出一种基于半监督回归深度学习模型的方法，直接从图像计算平纹画布的线密度，避免了耗时的交叉点分割步骤，并在多位艺术家作品上验证了其有效性。

2023-03-31cs.CVcs.LGeess.IV

更多相关论文收起更多论文

Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks

nucl.ai Research Laboratory（nucl.ai研究实验室）；Artificial Intelligence in Creative Industries（创意产业人工智能机构）

Convolutional neural networks (CNNs) have proven highly effective at image synthesis and style transfer. For most users, however, using them as tools can be a challenging task due to their unpredictable behavior that goes against common intuitions. This paper introduces a novel concept to augment such generative architectures with semantic annotations, either by manually authoring pixel labels or using existing solutions for semantic segmentation. The result is a content-aware generative algorithm that offers meaningful control over the outcome. Thus, we increase the quality of images generate…

2016-03-05cs.CV

A Neural Algorithm of Artistic Style

Werner Reichardt Centre for Integrative Neuroscience（维尔纳·赖卡特整合神经科学中心）；Institute of Theoretical Physics, University of Tübingen（图宾根大学理论物理研究所）；Bernstein Center for Computational Neuroscience（伯恩斯坦计算神经科学中心）；Max Planck Institute for Biological Cybernetics（马克斯·普朗克生物控制论研究所）；Baylor College of Medicine（贝勒医学院）；Graduate School for Neural Information Processing（神经信息处理研究生院）

In fine art, especially painting, humans have mastered the skill to create unique visual experiences through composing a complex interplay between the content and style of an image. Thus far the algorithmic basis of this process is unknown and there exists no artificial system with similar capabilities. However, in other key areas of visual perception such as object and face recognition near-human performance was recently demonstrated by a class of biologically inspired vision models called Deep Neural Networks. Here we introduce an artificial system based on a Deep Neural Network that creates…

2015-09-02cs.CVcs.NEq-bio.NC