A panel of volunteers was asked to look at images and the ads chosen to go alongside them and evaluate which ads they considered relevant enough to consider clicking on. "That test shows that we can, on average, produce one correct ad per three suggested ads," says Yang. He believes this is a high enough success rate to suggest the approach could work commercially. When the same users were shown randomly selected ads with images, only one in 50 was deemed relevant enough to be clicked.

Researchers at Microsoft Research Asia previously developed a system that used image analysis to classify photos into a handful of categories in order to refine the text-based selection of advertising. Yang's goal, he says, is to bring contextual advertising to pages with little or no text. This would require software capable of classifying images using a larger vocabulary, like the one he is developing.

The team is currently working to add thesaurus-like capabilities to its system, so it can generate multiple words to describe the same feature in an image, thereby increasing the number of relevant ads that can be found. It is already possible to have the software work on individual video frames. The group is also working on customizing it to work on video footage.

"This approach to contextual advertising is potentially very interesting for advertisers," says Debra Williamson, a senior analyst with the digital marketing and advertising research firm eMarketer. "On the Web today, advertising is built around the text on a page, even when the media at the center of people's attention is imagery or video."

If the technology is reliable enough, applying it to video would likely have more potential than for still images, says Williamson. For a long video, she says, "a short description can't represent everything in the footage. If you can scan what's in the video, you could choose adverts to display minute by minute based on what appears."