Picasso, Matisse, or a Fake? Automated Analysis of Drawings at the Stroke Level for Attribution and Authentication

This paper proposes a computational approach for analysis of strokes in line drawings by artists. We aim at developing an AI methodology that facilitates attribution of drawings of unknown authors in a way that is not easy to be deceived by forged art. The methodology used is based on quantifying the characteristics of individual strokes in drawings. We propose a novel algorithm for segmenting individual strokes. We designed and compared different hand-crafted and learned features for the task of quantifying stroke characteristics. We also propose and compare different classification methods at the drawing level. We experimented with a dataset of 300 digitized drawings with over 80 thousands strokes. The collection mainly consisted of drawings of Pablo Picasso, Henry Matisse, and Egon Schiele, besides a small number of representative works of other artists. The experiments shows that the proposed methodology can classify individual strokes with accuracy 70%-90%, and aggregate over drawings with accuracy above 80%, while being robust to be deceived by fakes (with accuracy 100% for detecting fakes in most settings)

Interpretive seeing: art in the archive

According to recent historical research trends, the iconography within art offers researchers new insight into past events, behaviors, and ideologies. Images tend to capture aspects of the past absent from textual records. Paintings and drawings have been employed by the United States army, past political leaders, and Western explorers to record the surrounding social, political, and/or physical environment. And, paintings often carry ideological arguments and critiques on the surrounding political and economic environment. These art records are creations and participants in the surrounding socio-political environment. As institutions of collective memory and preservers of public documents, archives are obligated to preserve and promote the documentary nature of the iconography within art. This thesis built upon studies in archives (Canadian and American), history, art history, and content-based image retrieval to argue that documentary art belongs in archival repositories. By accepting the documentary contributions of art, archivists serve to expand the documentary record and enhance our understanding of the past

Fine-grained painting classification

A lot of progress has been made in the domain of image classification in the deep learning era, however, not so much for paintings. Even though paintings are images they are very different from photographs and classification of paintings requires in-depth domain knowledge compared to classifying an object. This makes the task of fine-grained classification of paintings even harder. In this thesis, we evaluate the classification of paintings into its various styles, genres, artists and formulate the problem of dating paintings as a classification problem. We experiment with the standard networks available as baselines and then improve the classification models via multi-task learning. We also propose a novel architectural addition to the VGG network to do fine-grained classification. Our models beat the existing state-of-the-art classifiers by a big margin

Novel image descriptors and learning methods for image classification applications

Image classification is an active and rapidly expanding research area in computer vision and machine learning due to its broad applications. With the advent of big data, the need for robust image descriptors and learning methods to process a large number of images for different kinds of visual applications has greatly increased. Towards that end, this dissertation focuses on exploring new image descriptors and learning methods by incorporating important visual aspects and enhancing the feature representation in the discriminative space for advancing image classification. First, an innovative sparse representation model using the complete marginal Fisher analysis (CMFA-SR) framework is proposed for improving the image classification performance. In particular, the complete marginal Fisher analysis method extracts the discriminatory features in both the column space of the local samples based within class scatter matrix and the null space of its transformed matrix. To further improve the classification capability, a discriminative sparse representation model is proposed by integrating a representation criterion such as the sparse representation and a discriminative criterion. Second, the discriminative dictionary distribution based sparse coding (DDSC) method is presented that utilizes both the discriminative and generative information to enhance the feature representation. Specifically, the dictionary distribution criterion reveals the class conditional probability of each dictionary item by using the dictionary distribution coefficients, and the discriminative criterion applies new within-class and between-class scatter matrices for discriminant analysis. Third, a fused color Fisher vector (FCFV) feature is developed by integrating the most expressive features of the DAISY Fisher vector (D-FV) feature, the WLD-SIFT Fisher vector (WS-FV) feature, and the SIFT-FV feature in different color spaces to capture the local, color, spatial, relative intensity, as well as the gradient orientation information. Furthermore, a sparse kernel manifold learner (SKML) method is applied to the FCFV features for learning a discriminative sparse representation by considering the local manifold structure and the label information based on the marginal Fisher criterion. Finally, a novel multiple anthropological Fisher kernel framework (M-AFK) is presented to extract and enhance the facial genetic features for kinship verification. The proposed method is derived by applying a novel similarity enhancement approach based on SIFT flow and learning an inheritable transformation on the multiple Fisher vector features that uses the criterion of minimizing the distance among the kinship samples and maximizing the distance among the non-kinship samples. The effectiveness of the proposed methods is assessed on numerous image classification tasks, such as face recognition, kinship verification, scene classification, object classification, and computational fine art painting categorization. The experimental results on popular image datasets show the feasibility of the proposed methods

Hierarchical Classification of Paintings Using Face- and Brush Stroke Models

It is often difficult to attribute works of art to a certain artist. In the case of paintings, radiological methods like X-ray and infra-red diagnosis, digital radiography, computer-tomography, etc. and color analyzes are employed to authenticate works of art. But all these methods do not relate certain characteristics of an art work to a specific artist - the artist's personal style. In order to study this personal style, we examine the "structural signature " based on brush strokes in particular in portrait miniatures. A computer-aided classification and recognition system for portrait miniatures is developed, which enables a semi-automatic classification based on brush strokes. A hierarchically structured classification scheme is introduced which separates the classification into three different levels of information: color, shape of region, and structure of brush strokes

When Children Draw Gods

This open access book explores how children draw god. It looks at children’s drawings collected in a large variety of cultural and religious traditions. Coverage demonstrates the richness of drawing as a method for studying representations of the divine. In the process, it also contributes to our understanding of this concept, its origins, and its development. This intercultural work brings together scholars from different disciplines and countries, including Switzerland, Japan, Russia, Iran, Brazil, and the Netherlands. It does more than share the results of their research and analysis. The volume also critically examines the contributions and limitations of this methodology. In addition, it also reflects on the new empirical and theoretical perspectives within the broader framework of the study of this concept. The concept of god is one of the most difficult to grasp. This volume offers new insights by focusing on the many different ways children depict god throughout the world. Readers will discover the importance of spatial imagery and color choices in drawings of god. They will also learn about how the divine's emotional expression correlates to age, gender, and religiosity as well as strategies used by children who are prohibited from representing their god