Data Analytics for Cultural Heritage : Current Trends and Concepts / edited by Abdelhak Belhi, Abdelaziz Bouras, Abdulaziz Khalid Al-Ali, Abdul Hamid Sadka

Mitwirkende(r):

Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Englisch Verlag: Cham : Springer International Publishing, 2021Verlag: Cham : Imprint: Springer, 2021Auflage: 1st ed. 2021Beschreibung: 1 Online-Ressource(XV, 280 p. 190 illus., 122 illus. in color.)ISBN:

9783030667771

Schlagwörter:

006.3 23

DOI: DOI: 10.1007/978-3-030-66777-1Online-Ressourcen:

Zusammenfassung: Cultural data categorization -- Cultural heritage data sets -- Historical manuscript analysis -- Cultural repository analytics -- Cultural image in painting and completion -- Cultural image super resolution and visual curation -- Cultural object marching and link retrieval -- Natural language processing in the cultural and historical contexts -- Cultural ontology learning -- Data analytics applications for attractiveness and targeted advertising in cultural heritage.Zusammenfassung: This book considers the challenges related to the effective implementation of artificial intelligence (AI) and machine learning (ML) technologies to the cultural heritage digitization process. Particular focus is placed on improvements to the data acquisition stage, as well as the data enrichment and curation stages, using advanced artificial intelligence techniques and tools. An emphasis is placed on recent applications related to deep learning for visual recognition, generative models, natural language processing, and super resolution. The book is a valuable reference for researchers working in the multidisciplinary field of cultural heritage and AI, as well as professional experts in the art and culture domains, such as museums, libraries, and historic sites and buildings. Reports on techniques and methods that leverage AI and machine learning and their impact on the digitization of cultural heritage; Addresses challenges of improving data acquisition, enrichment and management processes; Highlights contributions from international researchers from diverse fields and subject areas.PPN: PPN: 1753053749Package identifier: Produktsigel: ZDB-2-INR | ZDB-2-SEB | ZDB-2-SXIT

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