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Data analytics for cultural heritage...

Belhi, Abdelhak.

Data analytics for cultural heritagecurrent trends and concepts /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data analytics for cultural heritageedited by Abdelhak Belhi ... [et al.].
Reminder of title:	current trends and concepts /
other author:	Belhi, Abdelhak.
Published:	Cham :Springer International Publishing :2021.
Description:	xv, 280 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Cultural propertyData processing.
Online resource:	https://doi.org/10.1007/978-3-030-66777-1
ISBN:	9783030667771$q(electronic bk.)

Data analytics for cultural heritagecurrent trends and concepts /
Data analytics for cultural heritagecurrent trends and concepts /[electronic resource] :edited by Abdelhak Belhi ... [et al.]. - Cham :Springer International Publishing :2021. - xv, 280 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9783030667771$q(electronic bk.)

Standard No.: 10.1007/978-3-030-66777-1doiSubjects--Topical Terms:

797579
Cultural property
--Data processing.

LC Class. No.: CC135 / .D38 2021

Dewey Class. No.: 930.10285

Data analytics for cultural heritagecurrent trends and concepts /
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505 0 $a 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.
520 $a 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.
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