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.

Currently computers have difficult recognizing objects in images. While practical solutions exist for a few simple classes such as human faces or cars, the more general problem of recognizing all different classes of objects in the world (e.g. guitars, bottles, telephones) remains unsolved. Computer Vision researchers are currently investigating methods that can recognize and localize thousands of different object categories in complex scenes. A key component of these algorithms is the data used to train the computers' model of each object. Current approaches use collections of images laboriously gathered by hand.

Our research explores how the billions of images available on the Internet can be used to train models for object recognition. With overwhelming amounts of data, many problems can be tackled with simple algorithms. We gathered from the web 79 million images. We are using this massive dataset to train a computer to recognize objects within an image and to understand the scenes depicted in photographs. Read More...

The internet is finally becoming what I always dreamed it would be... an open forum of ideas and knowledge that people are willing to freely share with others to make the world a little better. This guy is giving away useful and practical knowledge he learned the hard way so you don't have to. Mostly Windows and Linux info, with plenty of Vista tips. Good stuff. Click the pic for more. Read More...

Canova is a dual display touch screen notebook concept created by V12 Design, an industrial design and engineering studio in Milan. Canova is a notebook which has dual LCD touch screen display that can do almost everything from a sketch pad, music score, graph paper, watching a movie and so on. It has the same usage as other notebooks. The thing that makes it special is it going to have everything in touch screen, for example touch screen keyboard and it doesn't require any mouse. The electronic pen and dedicated hardware of the notebook brings the machine to life and make viewers glued to it making everything accessible on two massive touch-sensitive screens. Read More...

A Nice Round-up of steps (from Microsoft) that Windows XP users should take when they get a new computer.

click read more to see the entire text of the article, or click the pic to go to the original. Read More...