Conference proceeding
Visual Topical Analysis of Museum Collections
DIGITAL LIBRARIES: PROVIDING QUALITY INFORMATION, v 9469, pp 1-11
01 Jan 2015
Abstract
Museums are highly specialized cultural institutions. Obstacles exist between the knowledge and terminology of the museum professionals and that of the general public. Topical analysis of museum collections can reveal topical similarities and differences among museums and facilitate museum tours with recommended professional guides. In this study, 7177 French artworks collected by 90 art museums worldwide were investigated. The Self-Organizing Map (SOM) technique, an unsupervised artificial neural network method, was applied to visually analyze similarities and differences among the museums. The Treemap technique was also employed on a large dataset to reveal the distribution of the specific themes among the investigated museums. Finally, a comprehensive museum tour recommendation mechanism is established for tourists.
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Details
- Title
- Visual Topical Analysis of Museum Collections
- Creators
- Lu An - Wuhan UniversityLiqin Zhou - Wuhan UniversityXia Lin - Drexel UniversityChuanming Yu - Zhongnan University of Economics and Law
- Contributors
- R B Allen (Editor)J Hunter (Editor)M L Zeng (Editor)
- Publication Details
- DIGITAL LIBRARIES: PROVIDING QUALITY INFORMATION, v 9469, pp 1-11
- Series
- Lecture Notes in Computer Science
- Publisher
- Springer Nature
- Number of pages
- 11
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000375767400001
- Scopus ID
- 2-s2.0-84952019671
- Other Identifier
- 991019167543504721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Interdisciplinary Applications
- Computer Science, Theory & Methods
- Robotics