In this special guest feature, Dan Olds from Gabriel Consulting writes that a demo at this week’s GPU technology conference showed how Big Data powered by accelerated computing could change the face of retail.
NVIDIA CEO Jen-Hsun Huang’s GTC 2013 keynote was a typical whirlwind tour (with real wind, but that’s a different article) through all the various GPU-related worlds that NVIDIA is touching these days. These addresses are usually chock-full of demonstrations showing where we are in terms of state-of-the-art graphics, scientific and technical computing, entertainment, and now: finding dresses.
In this demonstration, Jen Hsun leafed through the latest edition of In Style magazine. While the models are svelte (or starved), the magazine definitely isn’t, weighing in with 594 pages of ads. A dress from one of those ads was chosen, its picture was taken, and it was sent off for image matching. What came back was a set of likely matches that the image-matching tool found via eBay. (This can be seen in the semi-blurry picture taken from my third-row perch.)
Hmm… now that I think about it, this technology probably isn’t confined only to dresses. With some minor technical tweaks (like checking different boxes), I imagine it would be quite possible to match many other items. I’m thinking handbags, blouses, shoes, skorts, and even jorts for those needing to feed their denim demons.
They also demonstrated that it’s possible to capture a particular pattern and then search for clothing that has the same, or a similar, look. To my untrained eye, it looked to do a pretty good job. It didn’t find exact matches, but the selection shown came pretty close to the mark.
The impressive thing about this tool is its accuracy and speed. On each demo it not only returned the correct type of garment, but the results were surprisingly close to the original image in terms of look and general configuration. And it took only a few seconds – not much longer than the loading time for a web page.
There are already a fair number of images on the Internet, and users of Facebook add something like 300 million more per day. On the video side, there’s something like 72 hours of video added to YouTube per minute. Over time, this is going to add up. There will be an acute need for more sophisticated image searching/matching technology.
So – aside from everyone who likes to shop for clothes, who will use this technology? The companies who want to make it quicker and easier for potential customers to comb through their vast inventories of goods. With our increasing reliance on communicating via images, the ability to search, sort, and match is going to become more important over time.