Cultural Visualization techniques
Presentation at Nowcasting Symposium, Design/Media Arts
Department, UCLA, Los Angeles, October 16-17, 2009
(original title: “Cultural Analytics annual report 6/2008-9/2009)
Dr. Lev Manovich
Director, Software Studies Initiative, Calit2 + UCSD
Professor, Visual Arts Department
manovich.lev@gmail.com
You can capture this lecture using any media and share it.
Follow our research: softwarestudies.com
cultural analytics research @Software Studies Initiative at
Calit2/UCSD - key people:
Dr. Jeremy Douglass | Postdoctoral Researcher
Tara Zepel | PhD student, Art History
Sunsern Cheamanunkul | PhD student, Computer Science
So Yamaoka | PhD student, Computer Science
William Huber | PhD student, Art History
for the expanded list of participants, see softwarestudies.com
Our research is made possible by the support from:
Center for Research in Computing and the Arts (CRCA)
California Institute for Telecommunication and
Information (Calit2)
NEH Office of Digital Humanities
National Energy Research Scientific Computing Center
Singapore Ministry of Education
Bergen University (Norway)
UCHRI
Software Studies Initiative Collaborators:
Yuri Tsivian, Department of Art History, University of Chicago: cinemetrics.lv | film
analysis
Adele Eisenstein: Digital Formalism project (Department for Theatre, Film and Media
Studies (TFM), Vienna University; the Austrian Film Museum; Interactive Media
Systems Group, Vienna University of Technology) | film analysis
Netherlands Institute for Sound and Vision | television and motion graphics
analysis
San Diego Museum of Contemporary Art | mapping art for an exhibition
Isabel Galhano Rodrigues, University of Porto, Portugal | gesture analysis
David Kirsh, Cognitive Science, UCSD | dance video analysis
Ph.D. students, Art History, UCSD | art history and visual culture
Jim Hollan, Cognitive Science, UCSD | visualization | cultural analytics software
Falko Kuester, Structural Engineering, UCSD + Calit2 | visual analytics | cultural
analytics software
Yoav Freund, Computer Science and Engineering, UCSD | machine learning and
machine vision | cultural analytics software
Kay O’Halloran, Multimodal Analysis Lab, National University of Singapore | Mapping
Asian Cultures project
Giorgos Cheliotis: Communication and New Media, National University of Singapore |
Mapping Asian Cultures project
Matthew Fuller: Goldsmiths College, University of London | software studies, game
studies
cultural analytics =
quantitative analysis and visualization of cultural
data
goals of cultural analytics:
- being able to better represent the complexity, diversity,
variability, and uniqueness of cultural processes and artifacts
- develop techniques to describe the dimensions of cultural
artifacts and cultural processes which until now received little
or no attention (such as gradual temporal change)
- create much more inclusive cultural histories and analysis ideally taking into account all available cultural objects
created in particular cultural area and time period (“art history
without names”)
- democratize cultural research by creating open-source tools
for cultural analysis and visualization
- create interfaces for exploration of cultural data which
operate across multiple scales - from details of structure of a
particular individual cultural artifact/processes to massive
cultural data sets/flows
cultural analytics - typical steps:
-1) description (i.e, “culture into data”):
a) manual: annotation, tagging
b) automatic: software analysis of media; capturing user activity
our focus: easy-to-use techniques for automatic description of visual
and interactive media
2) optional: statistical data analysis
3) data visualization (reduction, summarization) and data mapping
(expansion, outlining, layering)
our focus: new visualization + mapping techniques appropriate for
interactive exploration of large sets of visual objects
4) interpretation (humanities), or explanation (science), or
correlation (social science)
visualization of cultural data - visualization types:
1) visualization without doing additional annotation / automatic analysis
a) display all objects in a set together organized by exiting metadata (for instance, dates,
artist names, etc.)
b) sample and re-order (for instance: montage, slice)
2) visualization after doing additional data analysis/annotation
c) visualization of newly generated metadata (graph)
d) display objects organized by metadata (image graph)
examples:
d1) 2D sorted view
d2) 2D image graph - using single feature for each dimension
d3) 2D image graph - using combination of features (PCA, etc.)
our techniques:
“analytical browsing” interfaces which combine media browsing and
graphs to enable visual exploration of cultural
data
“The aim pursued with visual exploration is to give an overview of the data and to
allow users to interactively browse through different portions of the data. In this
scenario users have no or only vague hypotheses about the data; their aim is to
find some. In this sense, visual exploration can be understood as an undirected
search for relevant information within the data. To support users in the search process,
a high degree of interactivity must be a key feature of visual exploration techniques.”
Christian Tominski, Event-Based Visualization for User-Centered Visual Analysis, PhD
Thesis, Institute for Computer Science, Department of Computer Science and
Electrical Engineering, University of Rostock, 2006.
“Exploration denotes an undirected search for interesting features in a data set.”
Kreuseler, M., Nocke, T., and Schumann, H. A History Mechanism for Visual Data
Mining. In Proceedings of the IEEE Symposium on information Visualization
(infovis'04) - Volume 00 (October 10 - 12, 2004). INFOVIS. IEEE Computer Society,
Washington, DC, 49-56. 2004.
source: www.infovis-wiki.net.
Cultural Analytics software running on HIPerSpace (May 2009)
Cultural Analytics software running on HIPerSpace (May 2009)
Cultural Analytics software running on HIPerSpace (May 2009)