Visualizations of Impressionist artists - color histograms (part 2)

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Visualizations of Impressionist artists - color palettes (part 1) were created by UCSD undergraduate student Megan O'Rourke for her homework in my Winter 2012 class data visualization and compututional art history (the link is to Spring 2012 version of the class).

Here is another innovative visualization created by Megan. She adopted histogram technique to compare color palettes of six Impressionist artists. The histograms show the relative proportions of different hue in the set of paintings of each artist. (To make this visualization easier to read, below is the visualization of the same images from the earlier post. It maps paintings according to x-axis = median saturation, y-axis = average hue).

Together, the two visualizations reveal strong similarity between the color "footprints" of the selected paintings of these artists.








Data:
Images of 630 Impressionist paintings.

Source:
Artstor.

Number of paintings per artist:
Artstor contains only some of the paintings by these arists. The diffrences in the numbers of images available for each artist reflect the differences in popularity of each artist as well their varied productivity.

The histograms use the median hue values measured per each painting.

Visualizations of Impressionist artists - color palette comparisons (part 1)

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The following visualizations was created by UCSD undergraduate student Megan O'Rourke in my Winter 2012 class data visualization and compututional art history (the link is to Spring 2012 version of the class).

The visualizations compare color palettes in paintings of of six Impressionist artists. In each image plot, x-axis = median saturation; y-axis = median hue. The visualizations were created with our open source ImagePlot software. Click on each image to see high resolution version.

Data:
Images of 630 Impressionist paintings.
Source:
Artstor.
Number of paintings per artist:
Artstor contains only some of the paintings by these arists. The diffrences in the numbers of images available for each artist reflect the differences in popularity of each artist as well their varied productivity.





When I wrote a macro which we distribute with ImagePlot and included average hue as one the image measurements, I was not sure if it can be actually used in a meaningful way. Megan's visualization shows that this measurement is quite useful. Her other invention was to extend plots vertically and place them side by side - a really good format for comparing image sets.

Teaching of art history for many decades of the 20th century was built on comparing two images at a time using slide projector technology. Media visualization methods developed at our lab allow scaling of comparision. Any number of images can be placed side by side and sorted according to multiple attributes (including metadata, content and visual form).


Close-up: a comparison between Cassat, Monet, Morissot:




Looking at the visualizations, I am struck by how similar are the color footprints of the Impressionist artists. What are the reasons for this similarity? How does it relate to the range of subects in their works, and their habit of working outside using newly available technology of oil paint in tubes?


close-up: all 1226 Impressionist paintings:

visualizing explosion of digital data

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The World's Technological Capacity to Store, Communicate, and Compute Information.

Martin Hilbert1 and Priscila López.

Science, February 10, 2011.


Abstract:

We estimate the world's technological capacity to store, communicate, and compute information, tracking 60 analog and digital technologies during the period from 1986 to 2007. In 2007, humankind was able to store 2.9 × 1020 optimally compressed bytes, communicate almost 2 × 1021 bytes, and carry out 6.4 × 1018 instructions per second on general-purpose computers. General-purpose computing capacity grew at an annual rate of 58%. The world's capacity for bidirectional telecommunication grew at 28% per year, closely followed by the increase in globally stored information (23%). Humankind's capacity for unidirectional information diffusion through broadcasting channels has experienced comparatively modest annual growth (6%). Telecommunication has been dominated by digital technologies since 1990 (99.9% in digital format in 2007), and the majority of our technological memory has been in digital format since the early 2000s (94% digital in 2007).



Illustration from the article in Washington Post about this research:

Guide to visualizing image and video collections

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Guide to visualizing image and video collections


The guide describes the techniques used in our Software Studies Lab to explore large image and video sets.

Like our previously released ImagePlot software, these techniques use free ImageJ digital image analysis application with our custom macros.


The guide covers the following operations (software used is in brackets):

- Download and setup free ImageJ software used to prepare images and video for visualizations, and create visualizations.
- Automatically detect shots in a video (shotdetect).
- Output video as a sequence of frames (ImageJ).
- Automatically scale all images in a folder (ImageJ).
- Create "montage" and "slice" visualizations (ImageJ).
- Create "montage" and "slice" visualizations with diff. size images located in multiple folders (ImageMontage, ImageSlice).
- Use Unix commands to create a data file containing file paths to images located in multiple folders (Unix).

Data Visualization and Computational Art History - my 2012 Spring course syllabus, UCSD

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Data Visualization and Computational Art History

Course syllabus


Instructor: Lev Manovich

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.

"Visualization as a Method in Art History" - slides of my 10 min intro to 2012 CAA session

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Intro to CAA 2012 session "Visualization as a Method in Art History"

View more presentations from Lev Manovich

session info:

Information Visualization as a Research Method in Art History
Friday, February 24, 2:30 PM–5:00 PM
West Hall Meeting Room 502A, Level 2, Los Angeles Convention Center

Chairs:
Christian Huemer, Getty Research Institute;
Lev Manovich, University of California, San Diego

Visualizing the Ecology of Complex Networks in Art History
Maximilian Schich, Northeastern University

Geoinformatics and Art History: Visualizing the Reception of American Art in Western Europe, 1948-1968
Catherine Dossin, Purdue University

Interactive Mapping of the Agents of the Art Market in Europe (1550-1800)
Sophie Raux, Université Lille Nord de France

Visualizing Art, Law, and Markets
Victoria Szabo, Duke University

Lithics Visualization Project for Analysis of Patterns and Aesthetic Presentation
Georgia Gene Berryhill, University of Maryland, Tom Levy, UCSD, and Lev Manovich, UCSD and Calit2

Information Visualization and Museum Practice
Piotr Adamczyk, Google and University of Illinois, Urbana-Champaign

new article: Lev Manovich, "How to Follow Software Users? (Digital Humanities, Software Studies, Big Data)"

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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 articles 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.



Illustration: Heatmap of user eye movement superimposed over a website she is looking at.