Computational Formalism (Record no. 61687)

MARC details
000 -LEADER
fixed length control field 02278nam a22002297a 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240521102943.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 240521b |||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9780262545648
028 ## - DISTRIBUTOR NAME
Distributor Name : International Books Distributor
Distributor address : Flat No. 17, Prakash Apartment, 4405/2, 5, Ansari Road, Daryaganj, New Delhi
Bill Number : 120440
Bill Date : 10/05/2024
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 702.85 WAS
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Wasielewski, Amanda.
245 ## - TITLE STATEMENT
Title Computational Formalism
Remainder of title : Art History and Machine Learning.
Statement of responsibility, etc / Amanda Wasielewski.
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc , London
Name of publisher, distributor, etc : The MIT Press
Date of publication, distribution, etc , 2023.
300 ## - PHYSICAL DESCRIPTION
Extent xii, 188p.
Dimensions : 24cm.
365 ## - TRADE PRICE
Price amount 2422
520 ## - SUMMARY, ETC.
Summary, etc Though formalism is an essential tool for art historians, much recent art history has focused on the social and political aspects of art. But now art historians are adopting machine learning methods to develop new ways to analyze the purely visual in datasets of art images. Amanda Wasielewski uses the term “computational formalism” todescribe this use of machine learning and computer vision technique in art historical research. At the same time that art historians are analyzing art images in new ways, computer scientists are using art images for experiments in machine learning and computer vision. Their research, says Wasielewski, would be greatly enriched by the inclusion of humanistic issues.<br/>The main purpose in applying computational techniques such as machine learning to art datasets is to automate the process of categorization using metrics such as style, a historically fraught concept in art history. After examining a fifteen-year trajectory in image categorization and art dataset creation in the fields of machine learning and computer vision, Wasielewski considers deep learning techniques that both create and detect forgeries and fakes in art. She investigates examples of art historical analysis in the fields of computer and information sciences, placing this research in the context of art historiography. She also raises questions as which artworks are chosen for digitization, and of those artworks that are born digital, which works gain acceptance into the canon of high art.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Formalism
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Art historians
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computational formalism
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Amanda Wasielewski.
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Books
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Date acquired Source of acquisition Cost, normal purchase price Total Checkouts Full call number Barcode Date last seen Date checked out Price effective from Koha item type
    Dewey Decimal Classification     SNU LIBRARY SNU LIBRARY 21/05/2024 International Book Distributors 2422.00 1 702.85 WAS 30190 14/10/2025 11/09/2025 21/05/2024 Books
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