Recognizing Characters in Art History Using Deep Learning (original) (raw)
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PEARC '20: Practice and Experience in Advanced Research Computing, 2020
Art historians are interested in possible methods and visual criteria for determining the style and authorship of artworks. One approach, developed by Giovanni Morelli in the late nineteenth century, focused on abstracting, extracting and comparing details of recognizable human forms, although he never prescribed what exactly to look for. In this work, we asked what could a contemporary method like convolution networks contribute or reveal about such a humanistic method that is not fully determined, but that is also so clearly aligned with computation? Convolution networks have become very successful in object recognition because they learn general features to distinguish and classify large sets of objects. Thus, we wanted to explore what features are present in these networks that have some discriminatory power for distinguishing paintings. We input the digitized art into a large-scale convolutional network that was pre-trained for object recognition from naturalistic images. Because we do not have labels, we extracted activations from the network and ran K-means clustering. We contrasted and evaluated discriminatory power between shallow and deeper layers. We also compared predetermined features from standard computer vision techniques of edge detection. It turns out that the deep network individual feature maps are highly generic and do not easily map onto obvious authorship interpretations, but in the aggregate can have strong discriminating power that are intuitive. Although this does not directly test issues of attribution, the application can inform humanistic perspectives regarding what counts as features that make up visual elements of paintings.
Extracting and Analyzing Deep Learning Features for Discriminating Historical Art
Practice and Experience in Advanced Research Computing, 2020
Art historians are interested in possible methods and visual criteria for determining the style and authorship of artworks. One approach, developed by Giovanni Morelli in the late nineteenth century, focused on abstracting, extracting and comparing details of recognizable human forms, although he never prescribed what exactly to look for. In this work, we asked what could a contemporary method like convolution networks contribute or reveal about such a humanistic method that is not fully determined, but that is also so clearly aligned with computation? Convolution networks have become very successful in object recognition because they learn general features to distinguish and classify large sets of objects. Thus, we wanted to explore what features are present in these networks that have some discriminatory power for distinguishing paintings. We input the digitized art into a large-scale convolutional network that was pre-trained for object recognition from naturalistic images. Because we do not have labels, we extracted activations from the network and ran K-means clustering. We contrasted and evaluated discriminatory power between shallow and deeper layers. We also compared predetermined features from standard computer vision techniques of edge detection. It turns out that the deep network individual feature maps are highly generic and do not easily map onto obvious authorship interpretations, but in the aggregate can have strong discriminating power that are intuitive. Although this does not directly test issues of attribution, the application can inform humanistic perspectives regarding what counts as features that make up visual elements of paintings.
2018
FACES 2.0 is a face recognition application based on a method of machine learning known as deep neural networking. Funded by the Kress Foundation (with initial work funded by the National Endowment for the Humanities), it is designed to automatically test the degree of probability of a shared identification between different works of portrait art--that is, non-photographic portraits that are subject to the subjectivity of artistic interpretation. When used correctly with good images, it has the potential to match what is known with a given unknown--something that is unlikely to be accidental--and yields results that may be considered probable. In this, FACES has the potential to provide previously unnoticeable or unconfirmable information by contributing categories of quantifiable data for researchers to factor into their own analyses. The FACES website will be available sometime in January, 2018: http://faces.ucr.edu
Deep transfer learning for visual analysis and attribution of paintings by Raphael
Heritage Science, 2023
Visual analysis and authentication of artworks are challenging tasks central to art history and criticism. This preliminary study presents a computational tool for scholars examining and authenticating a restricted class of paintings, with a specific focus on the paintings of Raffaello Sanzio da Urbino, more popularly known as Raphael. We applied transfer learning to the ResNet50 deep neural network for feature extraction and used a support vector machine (SVM) binary classifier in support of authentication. Edge detection and analysis algorithms, considered to be crucial for capturing the essence of Raphael's artistic style, including the brushwork signatures, were also integrated and are used as an authentication tool. The machine learning approach we have developed demonstrates an accuracy of 98% in image-based classification tasks during validation using a test set of well known and authentic paintings by Raphael. Of course, a full authentication protocol relies on provenance, history, material studies, iconography, studies of a work's condition, and more. Our work, then, contributes to just a portion of a full authentication protocol. Our findings suggest that machine learning methods, properly employed by experts aware of context, may enhance and expand traditional visual analysis for problems in art authentication.
Deep Learning Approaches to Art Style Recognition in
2017
Foreword Computation for the work described in this report was supported by the DeiC National HPC Centre, SDU. The authors will be referring to themselves in first person plural. Counselling and the original project proposal has been delivered by Manfred Jaeger.
The Shape of Art History in the Eyes of the Machine
How does the machine classify styles in art? And how does it relate to art historians's methods for analyzing style? Several studies have shown the ability of the machine to learn and predict style categories, such as Renaissance, Baroque, Impressionism, etc., from images of paintings. This implies that the machine can learn an internal representation encoding discriminative features through its visual analysis. However, such a representation is not necessarily interpretable. We conducted a comprehensive study of several of the state-of-the-art convolutional neural networks applied to the task of style classification on 77K images of paintings, and analyzed the learned representation through correlation analysis with concepts derived from art history. Surprisingly, the networks could place the works of art in a smooth temporal arrangement mainly based on learning style labels, without any a priori knowledge of time of creation, the historical time and context of styles, or relations between styles. The learned representations showed that there are few underlying factors that explain the visual variations of style in art. Some of these factors were found to correlate with style patterns suggested by Heinrich Wölfflin (1846-1945). The learned representations also consistently highlighted certain artists as the extreme distinctive representative of their styles, which quantitatively confirms art historian observations.
Two-Stage Deep Learning Approach to the Classification of Fine-Art Paintings
IEEE Access, 2019
Due to the digitization of fine art collections, pictures of fine art objects stored at museums and art galleries became widely available to the public. It created a demand for efficient software tools that would allow rapid retrieval and semantic categorization of art. This paper introduces a new, two-stage image classification approach aiming to improve the style classification accuracy. At the first stage, the proposed approach divides the input image into five patches and applies a deep convolutional neural network (CNN) to train and classify each patch individually. At the second stage, the outcomes from the individual five patches are fused in the decision-making module, which applies a shallow neural network trained on the probability vectors given by the first-stage classifier. While the first stage categorizes the input image based on the individual patches, the second stage infers the final decision label categorizing the artistic style of the analyzed input image. The key factor in improving the accuracy compared to the baseline techniques is the fact that the second stage is trained independently on the first stage using probability vectors instead of images. This way, the second stage is effectively trained to compensate for the potential mistakes made during the first stage. The proposed method was tested using six different pre-trained CNNs (AlexNet, VGG-16, VGG-19, GoogLeNet, ResNet-50, and Inceptionv3) as the first-stage classifiers, and a shallow neural network as a second-stage classifier. The experiments conducted using three standard art classification datasets indicated that the proposed method presents a significant improvement over the existing baseline techniques. INDEX TERMS Fine art style recognition, painting classification, machine learning, multi-stage classification, transfer learning, digital humanities.
Object Classification in Images of Neoclassical Artifacts Using Deep Learning
2017
In this paper, we report on our efforts for using Deep Learning for classifying artifacts and their features in digital visuals as a part of the Neoclassica framework. It was conceived to provide scholars with new methods for analyzing and classifying artifacts and aesthetic forms from the era of Classicism. The framework accommodates both traditional knowledge representation as a formal ontology and data-driven knowledge discovery, where cultural patterns will be identified by means of algorithms in statistical analysis and machine learning. We created a Deep Learning approach trained on photographs to classify the objects inside these photographs. In a next step, we will apply a different Deep Learning approach. It is capable of locating multiple objects inside an image and classifying them with a high accuracy.
Deep Level Annotation for Painter Attribution on Greek Vases utilizing Object Detection
Proceedings of the 4th ACM International workshop on Structuring and Understanding of Multimedia heritAge Contents
Painter attribution is based on a variety of factors, oftentimes deeply buried in the details such as the brushstrokes of the ears or the eyes, which a painter might paint in a specific way. To get to this details, the images have to be examined carefully and intensively. Our work is focused on this phenomenon of painter attribution, investigating those details using supervised machine learning methods for image recognition that rely on a set representation. In this paper however, we are going to focus on one step of our work specifically: The annotation process. With such a focus on details, a dense and detailed, but also transparent annotation of the images is necessary. On one hand this is essential for our research, on the other hand however, it is very time consuming and requires a lot of human resources. Therefore we developed an ontology for the annotation of the images and a semi-automated workflow with object detection component using YOLOv3 and closely tied to our ontology. This way we were able to automate our processes as efficiently as possible while maintaining the complexity of our annotations. CCS CONCEPTS • Applied computing → Arts and humanities; • Computing methodologies → Machine learning; Object detection.
Melanie Althage, Martin Dröge, Torsten Hiltmann, Claudia Prinz (ed.), Digitale Methoden in der geschichtswissenschaftlichen Praxis: Fachliche Transformationen und ihre epistemologischen Konsequenzen: Konferenzbeiträge der Digital History 2023, 2023
AI opens new possibilities for processing and analysing large, heterogeneous historical data corpora in a semi-automated way. The Ottoman Nature in Travelogues (ONiT) project develops an interdisciplinary methodological framework for an AI-driven analysis of text-image relations in digitised printed material. In this paper, we discuss our results from the first project year, in which we explore the potential of multi-modal deep learning approaches for combined analysis of text and image similarity of "nature" representations in historical prints. Our experiments with OpenCLIP for zero-shot classification of prints from the ICONCLASS AI Test Set show the potential but also limitations of using pre-trained contrastive-learning algorithms for historical contents. Based on the results and our learnings, we discuss in which way computational, quantitative methods affect our underlying epistemology stemming from more traditional "analogue" methods. Our experiences confirm that interdisciplinary collaboration between historians and AI developers is important to adapt disciplinary conventions and heuristics for use in applied AI methods. Our main learnings are the necessity to differentiate between distinct visual features in historical images versus representations of "nature" that require interpretation, and to develop an understanding for the features an AI algorithm can be retrained to detect.