At the International World Wide Web Conference in Beijing, two Google researchers unveiled VisualRank, software they say will advance digital image searching on the Web the same way Google's PageRank software advanced Web page searches. VisualRank is an algorithm that blends image-recognition software methods with techniques that weigh and rank images that look the most similar. Most image searches are based on cues from the text associated with each image, and not on the actual content of the image itself. Image analysis is a largely unsolved problem in computer science, the Google researchers say. "We wanted to incorporate all of the stuff that is happening in computer vision and put it in a Web framework," says Google's Shumeet Baluja, who made the presentation along with Google researcher Yushi Jing. Their paper, "PageRank for Product Image Search," focuses on a subset of the images that Google has cataloged. The researchers concentrated on the 2,000 most popular product queries on Google's product search, and sorted the top 10 images from both its ranking system and the standard Google Image Search results. The research effort then used a team of 150 Google employees to create a scoring system for image "relevance." The researchers say VisualRank returned 83 percent less irrelevant images.