A Revision Control System for Image Editing in Collaborative Multimedia Design

Revision control is a vital component in the collaborative development of artifacts such as software code and multimedia. While revision control has been widely deployed for text files, very few attempts to control the versioning of binary files can be found in the literature. This can be inconvenient for graphics applications that use a significant amount of binary data, such as images, videos, meshes, and animations. Existing strategies such as storing whole files for individual revisions or simple binary deltas, respectively consume significant storage and obscure semantic information. To overcome these limitations, in this paper we present a revision control system for digital images that stores revisions in form of graphs. Besides, being integrated with Git, our revision control system also facilitates artistic creation processes in common image editing and digital painting workflows. A preliminary user study demonstrates the usability of the proposed system.Comment: pp. 512-517 (6 pages

Computational Intelligence in Healthcare

This book is a printed edition of the Special Issue Computational Intelligence in Healthcare that was published in Electronic

Fine Art Pattern Extraction and Recognition

This is a reprint of articles from the Special Issue published online in the open access journal Journal of Imaging (ISSN 2313-433X) (available at: https://www.mdpi.com/journal/jimaging/special issues/faper2020)

Deep learning approaches to pattern extraction and recognition in paintings and drawings: an overview

This paper provides an overview of some of the most relevant deep learning approaches to pattern extraction and recognition in visual arts, particularly painting and drawing. Recent advances in deep learning and computer vision, coupled with the growing availability of large digitized visual art collections, have opened new opportunities for computer science researchers to assist the art community with automatic tools to analyse and further understand visual arts. Among other benefits, a deeper understanding of visual arts has the potential to make them more accessible to a wider population, ultimately supporting the spread of culture

A deep learning approach to clustering visual arts

Clustering artworks is difficult for several reasons. On the one hand, recognizing meaningful patterns based on domain knowledge and visual perception is extremely hard. On the other hand, applying traditional clustering and feature reduction techniques to the highly dimensional pixel space can be ineffective. To address these issues, in this paper we propose DELIUS: a DEep learning approach to cLustering vIsUal artS. The method uses a pre-trained convolutional network to extract features and then feeds these features into a deep embedded clustering model, where the task of mapping the raw input data to a latent space is jointly optimized with the task of finding a set of cluster centroids in this latent space. Quantitative and qualitative experimental results show the effectiveness of the proposed method. DELIUS can be useful for several tasks related to art analysis, in particular visual link retrieval and historical knowledge discovery in painting datasets.Comment: Submitted to IJC

Understanding Art with AI: Our Research Experience

Artificial Intelligence solutions are empowering many fields of knowledge, including art. Indeed, the growing availability of large collections of digitized artworks, coupled with recent advances in Pattern Recognition and Computer Vision, offer new opportunities for researchers in these fields to help the art community with automatic and intelligent support tools. In this discussion paper, we outline some research directions that we are exploring to contribute to the challenge of understanding art with AI. Specifically, our current research is primarily concerned with visual link retrieval, artwork clustering, integrating new features based on contextual information encoded in a knowledge graph, and implementing these methods on social robots to provide new engaging user interfaces. The application of Information Technology to fine arts has countless applications, the most important of which concerns the preservation and fruition of our cultural heritage, which has been severely penalized, along with other sectors, by the ongoing COVID pandemic. On the other hand, the artistic domain poses entirely new challenges to the traditional ones, which, if addressed, can push the limits of current methods to achieve better semantic scene understanding

Deep convolutional embedding for digitized painting clustering

Clustering artworks is difficult because of several reasons. On one hand, recognizing meaningful patterns in accordance with domain knowledge and visual perception is extremely hard. On the other hand, the application of traditional clustering and feature reduction techniques to the highly dimensional pixel space can be ineffective. To address these issues, we propose a deep convolutional embedding model for clustering digital paintings, in which the task of mapping the input raw data to an abstract, latent space is optimized jointly with the task of finding a set of cluster centroids in this latent feature space. Quantitative and qualitative experimental results show the effectiveness of the proposed method. The model is also able to outperform other state-of-the-art deep clustering approaches to the same problem. The proposed method may be beneficial to several art-related tasks, particularly visual link retrieval and historical knowledge discovery in painting datasets

RECODE: Revision Control for Digital Images

Revision control is a vital component in the collaborative development of artifacts such as software code and multimedia. While revision control has been widely deployed for text files, very few attempts to control the versioning of binary files can be found in the literature. This can be inconvenient for multimedia applications that use a significant amount of binary data, such as images, videos, meshes, and animations. Existing strategies such as storing whole files for individual revisions or simple binary deltas, respectively consume significant storage and complex semantic information. To overcome these limitations, in this paper we present RECODE, a revision control system for digital images. It stores revisions in the form of a DAG (directed acyclic graph) where nodes represent editing operations, and edges describe the spatial and temporal relationships between operations. Being integrated with GitHub, the largest project hosting platform, RECODE also facilitates the artistic creation process of distributed teams with different workflows that include image editing and digital painting. A preliminary user study was performed to assess the perceived usability of the proposed system

Using an adaptive neuro-fuzzy inference system for the classification of hypertension

In this work, neuro-fuzzy systems are compared to standard machine learning algorithms to predict the hypertension risk level. Hypertension is a cardiovascular disease, which should be continuously monitored to avoid the worsening of its symptoms. Automatic techniques are useful to support the clinicians in this task, however, most of the machine learning techniques behave like black boxes, thus they are not able to explain how their results have been obtained. In the medical domain, this is a critical factor, and explainability is demanded. Neuro-fuzzy systems, that combine Neural Networks (NNs) and Fuzzy Inference Systems (FISs), are used to obtain explainable results. Moreover, to enhance the explanation, a feature selection method has been used to reduce the number of relevant features and thus the overall number of fuzzy rules. Qualitative analyses have shown comparable results between the machine learning methods and the neuro-fuzzy systems. However, the neuro-fuzzy systems are able to explain the hypertension risk level with only nine fuzzy rules, which are easy to interpret since they use linguistic terms

Editorial for Special Issue "Fine Art Pattern Extraction and Recognition"

Cultural heritage, especially the fine arts, plays an invaluable role in the cultural, historical, and economic growth of our societies. Works of fine arts are primarily developed for aesthetic purposes and are mainly expressed through painting, sculpture, and architecture. In recent years, owing to technological improvements and drastic cost reductions, a large-scale digitization effort has been made, which has led to the increasing availability of large, digitized fine art collections. Coupled with recent advances in pattern recognition and computer vision, this availability has provided, especially researchers in these fields, with new opportunities to assist the art community by using automatic tools to further analyze and understand works of fine arts. Among other benefits, a deeper understanding of the fine arts has the potential to make works more accessible to a wider population, both in terms of fruition and creation, thus supporting the spread of culture