Visual dictionary

[Tim Strzechowski]

This is nice, and certainly appreciated, but does it have any bearing on Pynchon?  If so, you may wish to give it some context so listers have an idea of why you posted this.  If not, listers frequently put "NP" (i.e., Not Pynchon) in the subject heading.  Thus anyone wishing to skip this thread may do so.

Being an unmoderated list, these small adjustments to posting help listers decide what to read and what to skip/save for later.  Just a suggestion, is all.  Thanks.

Tim



 -------------- Original message ----------------------
From: "David Morris" <fqmorris at gmail.com>
> http://people.csail.mit.edu/torralba/tinyimages/
> 
> 
> We present a visualization of all the nouns in the English language
> arranged by semantic meaning. Each of the tiles in the mosaic is an
> arithmetic average of images relating to one of 53,463 nouns. The
> images for each word were obtained using Google's Image Search and
> other engines. A total of 7,527,697 images were used, each tile being
> the average of 140 images. The average reveals the dominant visual
> characteristics of each word. For some, the average turns out to be a
> recognizable image; for others the average is a colored blob. The list
> of nouns was obtained from Wordnet, a database compiled by
> lexicographers which records the semantic relationship between words.
> Using this database, we extract a tree-structured semantic hierarchy
> which we use to arrange tiles within the poster. We tessellate the
> poster using the hierarchy so that the proximity of two tiles is given
> by their semantic distance. Thus the poster explores the relationship
> between visual and semantic similarity. For a large part of our
> language the two are closely correlated as shown by the extent of
> visual clustering within the poster. The large-scale groupings
> correspond to broad categories such as plants or people. Within the
> plant cluster, for example, tighter semantic groupings are visible
> such as flowers or trees. In turn each of these clusters contains
> further groupings all the way down to individual, highly specific
> nouns. The averaging within each tile removes the variation between
> images of a given word, enhancing the similarly between neighbors. By
> clicking on top of the map, you will see the word corresponding to
> that location, the average image and the first 16 images returned by
> the image search online tools.