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Google researchers say they have a software technology intended to do for digital images on the Web what the company’s original PageRank software did for searches of Web pages.Google VisualRank Prototype Image Search [nytimes.com]
On Thursday at the International World Wide Web Conference in Beijing, two Google scientists presented a paper describing what the researchers call VisualRank, an algorithm for blending image-recognition software methods with techniques for weighting and ranking images that look most similar.
“We wanted to incorporate all of the stuff that is happening in computer vision and put it in a Web framework,” said Shumeet Baluja, a senior staff researcher at Google, who made the presentation with Yushi Jing, another Google researcher. The company’s expertise in creating vast graphs that weigh “nodes,” or Web pages, based on their “authority” can be applied to images that are the most representative of a particular query, he said.
I'm betting they roll out an improved version
of the "indexing the spoken audio in videos"
systems that are now beginning to appear,
very shortly, as well.
concentrated on the 2000 most popular product queries
With a team of 150 Google employees, it created a scoring system for image “relevance.”
The researchers said the retrieval returned 83 percent less irrelevant images.
I don't understand how 150 employees scoring a set of 2000 images to reduce irrelvance by 83% = software technology
You know how on Flickr people draw boxes around parts of the image? Or facebook lets you tag people's names?
Now if you search for "boobie", you'll get square snippets of boobies cropped out of flickr images of friends and family.
Or if you search for "Marsha", they'll crop out Marsha from the facebook images. Brilliant.
Burak Gokturk -he developed computer vision algorithms at Canesta,Intel and BEKO. He has been in the vision field for more than 8 years,published more than 30 papers, and holds more than 15 patent applications. His computer vision algorithms have been prototyped and/or implemented in various computer vision products. He developed a 3D shape representation scheme, called "Random Orthogonal Shape Sections", which achieved one of the best recognition rates for the detection of colon cancer. His other research concentrated on tracking, detection and recognition of 3D shapes and faces, medical imaging and 3D image compression.