Skip to content
Google Image Labeler is a feature, in form of a game, of Google Image Search that allows the user to label random images to help improve the quality of Google’s image search results.
The user will be randomly paired with a partner who is online and using the feature. Users can be registered players who accumulate a total score over all games played, or guests who just play for one game at a time. Note that players come from around the world, some practicing their English, and both American and British English will be encountered (soccer vs. football). When an uneven number of players are online, a player will play against a prerecorded set of words.
As of May 4, 2007, some rules have changed. Over a 2 minute period, (formerly 90 seconds) the user and his/her 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 the user’s label matches the partner’s label, both will earn points and move on to the next image until time runs out. It is possible to pass on an image but both users must agree to do this. Matched images used to earn 100 points for both partners. Now the score is variable from 50 to 150 depending on the specificity of the answer. The 150 score is rare but 140 points will be awarded for a name or specific word, even if the word is spelled out in the image. Terms with low specificity like “trees” or “man” earn only 50 points. There has never been any screening for correctness, so that if both players type “Jupiter” for an image of Saturn, they would presumably both get 140 points.
Labels that have been agreed on by previous users may show on an “off limits” list and cannot be used in that round. Some players think that the game staggers appearance of the images, and that sometimes it takes the first words typed by one player to form an “off limits” list for the other player. In other words, the off limits words may be unilateral, asymmetrical. This would explain the rather frequent circumstance when it seems a partner can’t think of words like “car,” “bird,” or “girl.” Very rarely, at the end of the match it becomes obvious that one image was different for the two players. Perhaps this is simply an error, or perhaps it is a test to see how quickly people will pass when their descriptions do not match, but it may also be a mechanism implemented to view cheaters, if the words for the different images are similar. At times, one user’s computer will fail to load a new image, or continue to display the previous image shown. Times likes these also call for a mutual “pass” on the part of both players.
After the 120 second time expires, the game is over. The user can see the user name of the partner for the first time, their score for the game (with which both are credited) and their individual cumulative score to date. These are compared to the daily high scores for teams and the all-time individual high scores. Google is betting on users’ competitiveness to rack up high scores to swell the number of images ranked. I have been the highest ranking person a few times.
The game end screen also show all the images presenting during the game along the list of labels guessed by the other player. The images themselves then link to the websites where those images were found and can be explored for more information and to satisfy curiosity.
The game is not designed simply for fun. Though the feature is enjoyable for the users, it is also a clever way for Google to ensure that its keywords are matched to correct images. Each matched word will help Google to build an accurate database used when using the Google Image Search.
Without human tagging of images, Google Images search has in the past relied on the context of the image. For example, a photo that is captioned “Portrait of Bill Gates” might have “Bill Gates” associated as a possible search term. The Google Image Labeler relies on humans that tag the meaning or content of the image, rather than its context looking on at where the image was used. By storing more information about the image, Google stores more possible avenues of discovering the image in response to a user’s search.
