Better image-recognition software could have a range of applications, Li says. It could, for example, improve Internet search engines or automatically tag digital-image collections. Li believes it might also help scientists sort through large amounts of visual information: "Image classification is sometimes a need in scientific study. Without computer assistance, researchers have to manually classify images, and this process can be slow and fall behind the high throughput of new images."

The underlying algorithms could perhaps lend themselves to various other difficult computing tasks. "Similar approaches can be applied to video analysis and possibly other problems," Li adds.

Luis von Ahn, an assistant professor of computer science at Carnegie Mellon University, in Pittsburgh, PA, says the research is a "step in the right direction" but that the software's accuracy rate must be improved. He notes that images on sites like Flickr often contain very similar material. "The truth of the matter is that these images are largely all about the same thing--people mostly take pictures of other people," he says. "So just using the word 'people' already tags a large percentage of the images correctly."

Von Ahn also believes that humans could play a greater role in training vision-recognition algorithms. He runs a site called Peekaboom that turns tagging images into a game for two online players. As an image is slowly revealed, each player must race to find the right tag for it. This helps train von Ahn's software to identify images by focusing on key portions. So far, approximately 100,000 individual images have been classified using Peekaboom, von Ahn says.

Alexander Berg, a computer-vision expert based at the University of California, in Berkley, agrees that humans could help computers understand complex data better. He suggests that the tags that appear on sites like Flickr and YouTube, as well as on many blogs and news websites, could prove crucial to this endeavor in the future. "In general, image and video search is an area due for major strides," Berg says. "More and more data is online with some amount of human labeling."

It's an idea that is welcomed by Li: "The more reliable data we can access and use, the better."