Data Visualization and Computational Art History
UCSD
Spring 2012
Visual Arts Department,UCSD
undergraduate course: VIS 149 / ICAM 130: Special Topics
graduate course: VIS 219: Special Topics
Comparing van Gogh paintings done in Paris and Arles.
X-axis = median brightness. Y-axis=median saturation.
Software: ImagePlot (developed by Software Studies Initiative directed by Lev Manovich).
DOWNLOAD:
Lev Manovich. How to Follow Software Users? (Digital Humanites, Software Studies, Big Data).
Abstract:
Big data is the new media of 2010s. Like previous waves of computer technologies, it changes what it means to know something and how we can generate this knowledge. So far, all big data projects in digital humanities that I am aware of used digitized cultural artifacts from the past. If we want to apply the big data paradigm to the study of contemporary interactive software-driven media, we are facing fascinating theoretical questions and challenges. What exactly is “big data” in the case of interactive media? How do we study the interactive temporal experiences of the users, as opposed to only analyzing the code of software programs and contents of media files? This article provides possible answers to these questions and proposes a methodology for the study of interactive media as “big data.”
Reference: This new article is not published anywhere yet. If you want to reference it, use the URL of this post.
DOWNLOAD:
Lev Manovich. Media Visualization: Visual Techniques for Exploring Large Media Collections.
This new text presents the theory and the techniques of media visualization used in our lab, with the analysis of the examples.
Exploring visualization of 4525 Time maagzine covers on a super high resolution display.
Lev Manovich. Style Space: How to compare image sets and follow their evolution (part 2).
[august 4-14, 2011]
selected points (see complete text for details)
Many social and natural processes follow a familiar Bell curve (normal
distribution). What are the shapes of distributions of large cultural
data sets? Because humanists only recently started to work
with big data sets, it is too early to make any generalizations. However,
it would not be surprising if the distributions of features of
very large cultural sets do follow the Bell curve pattern:
a dense cluster containing most of the data, gradually falling off to the side,
and a large very sparse area.
If we want to visually compare two or more image sets to each other in
relation to two visual properties, we can project them into a 2D space
defined by these visual properties as we did with Piet Mondrian’s and
Mark Rothko’s paintings in part 1. Using min and max values of the measured
properties of all images in out sets combined as the boundaries of the
visualization will allow us to use the visualization area most
efficiently. However, if we want to understand the footprint of each image set in
relation to the absolute mix and max - i.e. lowest and highest
possible values of visual features of all possible images - we need to
map our images differently.
A related idea is to render parts of an image set over the background showing
the complete set. This allows us to see the footprint of the these parts
in relation to the larger footprint of all images. For example, we can compare
pages of two manga titles from our complete set
of 883 titles comprising 1,074,790 pages.
new article:
Lev Manovich.
Style Space: How to compare image sets and follow their evolution (part 1)
[august 4-6, 2011]
selected points (see complete text for details)
A style space is a projection of quantified properties of a
set of cultural artifacts (or their parts) into a 2D place. X and Y
represent the properties (or their combinations). The position of
each artifact is determined by its values for these properties.
We are not claiming that such representations can capture all
aspects of a visual styke. A “style space” representation is
a tool for exploring image sets. It is particularly effective for
large sets.) It allows us compare all images in a set (or sets).
according to their visual values.
Separating a “style” into distinct visual dimensions and
organizing images according to their values on these dimensions
allows us to see more clearly how differences between the images
in a set. Visual differences are translated into spatial distances.
Images which are visually similar will be close; images which
are different will be further away.
Lev Manovich, July 21, 2011
keywords: search, Google, knowledge discovery, digital library, database, classification, folksonomy, information retrieval, HCI, interface, information visualization, digital humanities, cultural analytics, visual analytics, software studies, Manovich
Early 21st century humanities and media studies researchers have access to unprecedented amounts of media – more than they can possibly study, let alone simply watch or even search. (For examples of large media collections, see the list of repositories made available to the participants of Digging Into Data 2011 Competition, www.diggingintodata.org). The basic method of humanities and media studies which worked fine when the number of media objects were small – see all images or video, notice patterns, and interpret them – no longer works. For example, how do you study 167,00 images on Art Now Flickr gallery, 236,000 professional design portfolios on coroflot.com (both numbers as of 7/2011), or 176,000 Farm Security Administration/Office of War Information photographs taken between 1935 and 1944 digitized by Library of Congress (http://www.loc.gov/pictures/)?
Given the size of typical contemporary digital media collections, simply seeing what’s inside them is impossible.
Although it may appear that the reasons for this are the limitations of human vision and human information processing, I think that it is actually the fault of current interface designs and web technology. Standard interfaces for massive digital media collections such as list, gallery, grid, and slide do now allow us to see the contents of a whole collection. These interfaces usually they only display a few items at a time (regardless of whether you are in a browing mode, or in a search mode). This access method does not allow us to understand the “shape” of overall collection and notice interesting patters.
The popular media access technologies of the 19th and 20th century such as slide lanterns, film projectors, microfilm readers, Moviola and Steenbeck, record players, audio and video tape recorders, VCR, and DVD players were designed to access single media items at a time at a limited range of speeds. This went hand in hand with the media distribution mechanisms: record and video stores, libraries, television and radio would all only make available a few items at a time. For instance, you could not watch more than a few TV channels at the same time, or borrow more than a few videotapes from a library. At the same time, hierarchical classification systems used in library catalogs made it difficult to browse a collection or navigate it in orders not supported by catalogs. When you walked from shelf to shelf, you were typically following a classiffication based on subjects, with books organized by author names inside each category.
Together, these distribution and classification systems encouraged 20th century media researchers to decide before hand what media items to see, hear, or read. A researcher usually started with some subject in mind – films by a particular author, works by a particular photographer, or categories such as “1950s experimental American films” and “early 20th century Paris postcards.” It was impossible to imagine navigating through all films ever made or all postcards ever printed. (One of the the first media projects which organizes its narrative around navigation of a media archive is Jean-Luck Godard’s “Histoire(s) du cinéma” which draws samples from hundreds of films. ) The popular social science method for working with larger media sets in an objective manner – content analysis, i.e. tagging of semantics in a media collection by several people using a predefined vocabulary of terms also requires that a researcher decide before hand what information would be relevant to tag.
Unfortunately, the current standard in media access – computer search – does not take us out of this paradigm. Search interface is a blank frame waiting for you to type something. Before you click on search button, you have to decide what keywords and phrases to search for. So while the search brings a dramatic increase in speed of access, it assumes is that you know beforehand something about the collection worth exploring further.
We need the techniques for efficient browsing of content and discovery of patterns in massive media collections. Consider this defintion of “browse”: “To scan, to casually look through in order to find items of interest, especially without knowledge of what to look for beforehand” (“Browse”, Wiktionary). Consider also one of the meanings of the word “exploration”: “to travel somewhere in search of discovery” (“Exploration”, Wiktionary.) How can we discover interesting things in massive media collections? I.e., how can we browse through them efficiently and effectively, without a knowledge of what we want to find?
I found that academia.edu has many of my papers, and also PDF of my 2001 book manuscript (before it was copy edited):
New media theory and software studies needs basic terms.
For example, how do we call a person who is interacting with digital media?
User? No good.. “interactor”?
In 20rh century cultural theory dealt with viewers, readers, listeners, participants. In 21st century we don’t know who to call people we study. (game studies have “players” so at least they have this figured out
Thinking more about it, I realized that we can’t have a single good term to describe what we do with digital media for a reason.
In the 1960s-1970s digital media pioneers like Alan Kay systematically simulated most existing mediums in a computer. Computers, and various computing devices which followed (such as “smart” phones)came to support reading, viewing, participating, playing, remixing, collaborating.. and also many new functions.
This is why 20th century term s- reader, viewer, participant, publisher, player, user - all apply.
This multiplicity of media experiences is one of the defining characteristics of digital media - or, as Alan Kay called it, “the computer metamedium.”
Computational Culture, a journal of software studies is an online open-access peer-reviewed journal of inter-disciplinary enquiry into the nature of computational cultural objects, practices, processes and structures.
The journal’s primary aim is to examine the ways in which software undergirds and formulates contemporary life. Computational processes and systems not only enable contemporary forms of work and play and the management of emotional life but also drive the unfolding of new events that constitute political, social and ontological domains. In order to understand digital objects such as corporate software, search engines, medical databases or to enquire into the use of mobile phones, social networks, dating, games, financial systems or political crises, a detailed analysis of software cannot be avoided.
A developing form of literacy is required that matches an understanding of computational processes with those traditionally bound within the arts, humanities, and social sciences but also in more informal or practical modes of knowledge such as hacking and art.
The journal welcomes contributions that address such topics and many others that may derive and mix methodologies from cultural studies, science and technology studies, philosophy of computing, metamathematics, computer science, critical theory, media art, human computer interaction, media theory, design, philosophy.
Computational Culture publishes peer-reviewed articles, special projects, interviews, and reviews of books, projects, events and software. The journal is also involved in developing a series of events and projects to generate special issues.
The Editorial Group
Matthew Fuller, Goldsmiths
Andrew Goffey, Middlesex
Olga Goriunova, London Metropolitan
Graham Harwood, Goldsmith
Adrian Mackenzie, Lancaster
http://computationalculture.net
For initial enquiries, please contact: m.fullerATgold.ac.uk
.prc version of Software Takes Command (11.20.2008 version) is now available:
PRC | version for Kindle and other e-readers
(.pdf and .doc versions are available on softwarestudies.com/softbook )
This format is readable not only by Kindle and the recent Kindle Apps, but also by many other e-readers.
The transcoding was done by
Zoltán Dragon
Assistant Professor
Department of American Studies
Institute of English & American Studies
University of Szeged, Hungary
Many thanks, Zoltán!
