Developing New Ways to Search for Web Images by Shar Steed.
From the post:
Collections of photos, images, and videos are quickly coming to dominate the content available on the Web. Currently internet search engines rely on the text with which the images are labeled to return matches. But why is only text being used to search visual mediums? These labels can be unreliable, unhelpful and sometimes not available at all.
To solve this problem, scientists at Stanford and Princeton have been working to “create a new generation of visual search technologies.” Dr. Fei-Fei Li, a computer scientist at Stanford, has built the world’s largest visual database, containing more than 14 million labeled objects.
A system called ImageNet, applies the data gathered from the database to recognize similar, unlabeled objects with much greater accuracy than past algorithms.
A remarkable amount of material to work with, either via the API or downloading for your own hacking.
Another tool for assisting in the authoring of topic maps (or other content).