Publications

Technological tools for the conservation of silk heritage:  Improving the conservation of European Religious Textile Cultural Heritage

This paper presents the interdisciplinary H2020 SILKNOW project coordinated by the Universitat de València with researchers from the ICT and SSH fields. SILKNOW is a three-year project funded by the EU’s Horizon 2020 Programme under the two-stage call SC6-CULT-COOP-09 “European cultural heritage, access and analysis for a richer interpretation of the past”. The consortium has a total of nine partners from six different European countries (Spain, France, Germany, Slovenia, Poland and Italy). There are a total of three universities, two SMEs, one international institution, and three research institutes. In this paper, we introduce SILKNOW which has as a goal to promote, conserve and disseminate silk textiles; secondly, we introduce a set of interactive tools related to the project that will especially enhance conservation of this heritage.

Alba, Ester, Marcos Fernández, Mar Gaitán, Arabella León, Cristina Portalés, Jorge Sebastián, and Javier Sevilla. ‘Technological Tools for the Conservation of Silk Heritage: Improving the Conservation of European Religious Textile Cultural Heritage’. In 11th European Symposium on Religious Art, Restoriation and Conservation, 11:151–54. València: Kermes books, 2019.


Multi-task deep learning with incomplete training samples for the image-based prediction of variables describing silk fabrics

This paper presents a method for the classification of images of silk fabrics with the aim to predict properties such as the place and time of origin and the production technique. The proposed method was developed in the context of the EU project SILKNOW (http://silknow.eu/). In the context of classification, we address the problem of limited as well as not fully labelled data and investigate the connection between the distinct variables. A pre-trained Convolutional Neural Network (CNN) is used for the feature extraction and a classification network realizing Multi-task learning (MTL) is trained based on these features. The training procedure is adapted to enable the consideration of images that do not have a label for all tasks. Additionally, MTL with fully labelled training data is investigated for the classification of silk fabrics. The impact of both MTL approaches is compared to single-task learning based on two different class structures. We achieve overall accuracies of 92–95 % and average F1-scores of 88–90 % in our best experiments.

Dorozynski, M., Clermont, D., and Rottensteiner, F.: MULTI-TASK DEEP LEARNING WITH INCOMPLETE TRAINING SAMPLES FOR THE IMAGE-BASED PREDICTION OF VARIABLES DESCRIBING SILK FABRICS, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W6, 47–54, https://doi.org/10.5194/isprs-annals-IV-2-W6-47-2019, 2019.


Towards the Preservation and Dissemination of Historical Silk Weaving Techniques in the Digital Era

The aim of this paper is to propose a mathematical modelling of historical weaving techniques by means of matrices in order to be easily mapped to a virtual 3D representation. The work focuses on historical silk textiles, ranging from the 15th to the 19th centuries. We also propose a computer vision-based strategy to extract relevant information from digital imagery, by considering different types of images (textiles, technical drawings, and macro images).

Gaitán, M.; Alba, E.; León, A.; Pérez, M.; Sevilla, J.; Portalés, C. Towards the Preservation and Dissemination of Historical Silk Weaving Techniques in the Digital Era. Heritage 2019, 2, 1892-1911.


A Proposal to Model Ancient Silk Weaving Techniques and Extracting Information from Digital Imagery – Ongoing Results of the SILKNOW Project

Three dimensional (3D) virtual representations of the internal structure of textiles are of interest for a variety of purposes related to fashion, industry, education or other areas. The modeling of ancient weaving techniques relevant to understand and preserve our heritage, both tangible and intangible. However, ancient techniques cannot be reproduced with standard approaches, which usually are aligned with the characteristics of modern, mechanical looms. The aim of this paper is to propose mathematical modeling of ancient weaving techniques by means of matrices in order to be easily mapped to a virtual 3Drepresentation. The work focuses on ancient silk textiles, ranging from the 15thto the 19th centuries. We also propose a computer vision-based strategy to extract relevant information from digital imagery, by considering different types of images (textiles, technical drawings, and macro images.

Portalés, C.; Sevilla, J.; Pérez, M.; León, A. A Proposal to Model Ancient Silk Weaving Techniques and Extracting Information from Digital Imagery – Ongoing Results of the SILKNOW Project.; Faro (Portugal), 2019.


SILKNOWViz: Spatio-temporal data ontology viewer

Interactive visualization of spatio-temporal data is a very active area that has experienced remarkable advances in the last decade. This is due to the emergence of fields of research such as big data and advances in hardware that allow better analysis of information. This article describes the methodology followed and the design of an open source tool, which in addition to interactively visualizing spatio-temporal data that are represented in an ontology, allows the definition of what to visualize and how to do it. The tool allows selecting, filtering and visualizing in a graphical way the entities of the ontology with spatiotemporal data, as well as the instances related to them. The graphical elements used to display the information are specified on the same ontology, extending the VISO graphic ontology, used for mapping concepts to graphics objects with RDFS/OWL Visualization Language (RVL). This extension contemplates the data visualization on rich real-time 3D environments, allowing different modes of visualization according to the level of detail of the scene, while also emphasizing the treatment of spatio-temporal data, very often used in cultural heritage models. This visualization tool involves simple visualization scenarios and high interaction environments that allow complex comparative analysis. It combines traditional solutions, like hypercube or time-animations with innovative data selection methods.

Sevilla, J.; Portalés, C.; Gimeno, J.; Sebastián, J. SILKNOWViz: Spatio-temporal data ontology viewer.; Faro (Portugal), 2019.


Deep Learning zur Analyse von Bildern von Seidenstoffen für Anwendungen im Kontext der Bewahrung des kulturellen Erbes

Der vorliegende Beitrag befasst sich mit der Klassifikation von Bildern mittels Deep Learning. Exemplarisch wird hier die bildbasierte Prädiktion der Entstehungs-epoche von Seidenstoffen und Fertigungsskizzen betrachtet. An diesem Beispiel wird die Problemstellung von nicht eindeutig definierten Klassen sowie uneindeutigen Trainingsbei-spielen aufgegriffen und ein Lösungsansatz vorgestellt. Dieser basiert auf einer erweiterten Verlustfunktion, mit welcher die uneindeutigen Trainingsbeispiele während des Trainings genutzt werden können. Hierzu werden während der Trainingsphase übergreifende Klassen definiert. Die durchgeführten Experimente zeigen, dass hiermit die Genauigkeit der Klassi-fikation ohne die übergreifenden Klassen verbessert werden kann. Da der exemplarische Datensatz relativ wenig Daten umfasst wird ein vortrainiertes Convolutional Neural Network verwendet und eine Augmentierung der Daten durchgeführt. Der Einfluss der Aug-mentierung sowie der entwickelten Verlustfunktion wird in einer Kreuzvalidierung evaluiert.

Dorozynski, M.; Wittich, D.; Rottensteiner, F. (2019): Deep Learning zur Analyse von Bildern von Seidenstoffen für Anwendungen im Kontext der Bewahrung des kulturellen Erbes. 39. Wissenschaftlich-Technische Jahrestagung der DGPF und Dreiländertagung der OVG, DGPF und SGPF in Wien, Publikationen der DGPF Band 28, 387-399.


Innovación social en patrimonio cultural y museos de la seda en Europa: una mirada conectada con las industrias creativas

Esta contribución se enmarca dentro del proyecto H2020 Silknow que coordina la Universitat de València a través de un equipo multidisplicinar conformado por investigadores de las TIC y del ámbito de las humanidades y de las ciencias sociales. Hoy el concepto de patrimonio cultural está más que nunca ligado al presente, como punto de partida que nos vincula con nuestro pasado cultural a través del acto consciente de preservar y legar a las generaciones futuras, convirtiendo a la sociedad en custodia. Este acto emerge con la ambición de permanecer en un mundo cada vez más cambiante y mutable, pero en medio de esta corriente surge cada vez con más presencia la apreciación del patrimonio no solo a través de su poder comunicativo que nos liga con nuestro pasado, sino también con su potencial como propiciador de economías innovadoras, a través del desarrollo sostenible y el impulso de las industrias creativas. Centrado en el patrimonio de la seda, se analiza este pasado desde el presente y como motor de desarrollo futuro.

Alba, E. ; Pitarch, M.D; Sebastian, J.; Arnandis, R.; Portalés, C.; Gaitán, M. (2019). Innovación social en patrimonio cultural y museos de la seda en Europa: una mirada conectada con las industrias creativas. In. Boix, R. International Conference on Regional Science. Hacia un modelo económico más social y sostenible. Asociación Española de Ciencia Regional, Valencia.


Interactive Tools for the Preservation, Dissemination and Study of Silk Heritage—An Introduction to the SILKNOW Project

Silk was a major factor for progress in Europe, mostly along the Western Silk Road’s network of production and market centers. The silk trade also allowed for the exchange of ideas and innovations, having impacts at economic, technical, functional, cultural and symbolic levels. However, silk has today become a seriously endangered heritage. Although many European specialized museums are devoted to its preservation, they usually lack the size and resources to take advantage of state-of-the-art digital technologies. The aim of this paper is twofold; firstly, we introduce SILKNOW, an interdisciplinary project that has been recently funded by the H2020 Programme of the European Union in order to preserve and promote the heritage of silk textiles; secondly, we introduce a set of interactive tools related to the project.

Portalés, C.; Sebastián, J.; Alba, E.; Sevilla, J.; Gaitán, M.; Ruiz, P.; Fernández, M. Interactive Tools for the Preservation, Dissemination and Study of Silk Heritage—An Introduction to the SILKNOW Project. Multimodal Technologies Interact. 2018, 2, 28.


Pintores y ornatos para los Tejidos de seda en la Ilustración y la Academia valenciana de Bellas Artes

Durante el siglo XVIII, Valencia se convirtió en el principal centro manufacturero español de tejidos de seda. Con el fin de proporcionar modelos originales para los mismos se creó en el seno de la Academia de Bellas Artes de San Carlos la “Escuela de Flores, Ornatos y otros diseños adecuados para Tejidos” en 1784. El presente artículo atiende a sus precedentes, sus principales protagonistas y analiza su funcionamiento, además de considerar la relación que mantuvo a lo largo de su trayectoria con la industria de la seda.

López Terrada, M.J; Alba, E. (2018). Pintores y ornatos para los Tejidos de seda en la Ilustración y la Academia valenciana de Bellas Artes, Quaderns de Filologia: Estudis Literaris XXIII