Nearly a decade ago, Google unveiled an algorithm called PageRank, reinventing the way we search for web pages. Now, the company says, it has a technology that can do much the same for online image search.
Last week, at the International World Wide Web Conference in Beijing, two Google-affiliated researchers presented a paper called "PageRank for Product Image Search," trumpeting a fledging algorithm that overhauls the primitive text-based methods used by the company's current image search technologies.
Of course, the most recent Google Image Search results are often rubbish. Currently, when ranking images, the big search engines spend little time examining the images themselves. Instead, they look at the text surrounding those images.
By contrast, Google's PageRank for Product Image Search - also known as "VisualRank" - seeks to actually understand what's pictured. But the technology goes beyond classic image recognition, which can be time consuming and/or expensive - and which often breaks down with anything other than faces and a handful of other image types. In an effort to properly identify a wider range of objects, Baluja and Jing have merged existing image processing techniques with the sort of "link analysis" made famous by PageRank.
"Through an iterative procedure based on the PageRank computation, a numerical weight is assigned to each image," they explain. "This measures its relative importance to the other images being considered."
With classic image recognition, you typically take a known image and compare it to other images. You might use a known photo of Paris Hilton, for instance, to find other Paris pics. But VisualRank takes a different tack. Google's algorithm looks for "visual themes" across a collection of images, before ranking each image based on how well it matches those themes.
As an example, the researchers point to an image search on the word "McDonald's." In this case, VisualRank might identify the famous golden arches as theme. An image dominated by the golden arches would then be ranked higher than a pic where the arches are tucked into the background.
Baluja and Jing recently tested their algorithm using images retrieved by Google's 2000 most popular product searches, and a panel of 150 people decided that VisualRank reduced the number of irrelevant results by 83 per cent. The question is whether this could be applied to Google's entire database of images.
At the moment, this is just a research paper. And Google isn't the first to toy with the idea of true image search. After launching an online photo sharing tool that included face and character recognition, the Silicon Valley based Riya is now offering an image-rec shopping engine, known as Like.com, that locates products on sale across the web. And the transatlantic image rec gurus at Blinkx are well on their way with video search. ®