Google has debuted a service designed to improve the quality of the image query results returned by the search engine. Google Image Labeler is presented as a new feature integrated with Google Image Search permitting users to deliver subjective and contextual labels to images that are randomly presented to them. "You'll be randomly paired with a partner who's online and using the feature. Over a 90-second period, you and your partner will be shown the same set of images and asked to provide as many labels as possible to describe each image you see. When your label matches your partner's label, you'll earn some points and move on to the next image until time runs out. After time expires, you can explore the images you've seen and the websites where those images were found. And we'll show you the points you've earned throughout the session," explained Google.

At the basis of Google Image Labeler Beta is the ESP Game developed by Luis von Ahn from the Carnegie Mellon, and is a vivid example of crowd sourcing. With Google Image Labeler Beta, the Mountain View Company aims to leverage its audience via voluntary 90 seconds long rounds involving two randomly matched users. While designing tags to increase image search relevance, the users are awarded points and can move up into an All-time Top Contributors ranking. "Just an interest in helping Google improve the relevance of image search results for users like yourself. Although you do not have to log in to your Google account to help, logging in will allow you to keep track of your points. You can also choose to provide a nickname, or you can remain anonymous," stated the Mountain View Company.