One of our favorite aspects of Google web search are the informative snippets that appear with each search result.  The more relevant they are, the quicker we can find what we're looking for.  As members of the computer vision team, we're pleased to say that we now apply a similar philosophy to choosing thumbnails for videos on YouTube.  After all, the thumbnail is the first visual piece of information our users get when searching or browsing YouTube videos.  As we recently announced in a post on the YouTube Blog, our previous system of choosing thumbnails from the 25, 50 and 75% marks in the video, which often led to arbitrary, uninformative or sometimes even misleading images, is now a thing of the past.  When a new video comes to YouTube, we now analyze it with an algorithm whose aim is to pick a set of images that are visually representative of the content of the video.  As with the ground-breaking video identification tools that we launched on YouTube last year, this system is another example of how looking inside the video can lead to a safer, more relevant experience for our users.  

Launching a computer vision algorithm on YouTube comes with a unique set of challenges, many of which have to do with the incredible diversity of the content and the awe-inspiring rate at which it pours into our servers: each minute YouTube receives as much as 13 hours of video content in a wide variety of genres, styles, formats and resolutions.  Of course, even with some of the technical challenges aside, our work is far from finished.  We'll keep on studying how our users interact with thumbnails and, more generally, with video content on the web, and we'll continue thinking of innovative ways to use computer vision and machine learning to improve the experience.