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using "Random Bounce Me", a website that selects a page at random from the Google database. "Random Bounce Me" was queried repeatedly, each time collecting all JPEG and GIF images in the random page, except for images that did not fit the criteria: blank images, images that consist of a single color, images that are smaller than 20 pixels on either dimension, and images with an aspect ratio greater than 4.5 or smaller than 1/4.5. This process was repeated until 350,000 images were collected. The images were then rescaled to fit the game's display. Fifteen different images from the 350,000 are chosen for each session of the game.
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the game, it will have no taboo words. If the image is ever used again, it will have one taboo word: the word that resulted from the previous agreement. The next time the image is used, it will have two taboo words, and so on. "Taboo" words is done automatically by the system: once an image has been labeled enough times with the same word, that word becomes taboo so that the image will get a variety of different words as labels.
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Ahn has described countermeasures which prevent players from "cheating" the game, and introducing false data into the system. By giving players occasional test images for which common labels are known, it is possible to check that players are answering honestly, and a player's guesses are only stored
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Some images have "taboo" words; that is, words that cannot be entered as possible labels. These words are usually related to the image and make the game harder as they prevent common words to be used to label the image. Taboo words are obtained from the game itself. The first time an image is used in
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The applications and uses of having so many labeled images are significant; for example, more accurate image searching and accessibility for visually impaired users, by reading out an image's labels. Partnering two people to label images makes it more likely that entered words will be accurate. Since
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Occasionally, the game will be played solo, without a human partner, with the ESP Game itself acting as the opponent and delivering a series of pre-determined labels to the single human player (which have been harvested from labels given to the image during the course of earlier games played by real
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Once logged in, a user is automatically matched with a random partner. The partners do not know each other's identity and they cannot communicate. Once matched, they will both be shown the same image. Their task is to agree on a word that would be an appropriate label for the image. They both enter
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ESP game authors presented evidence that the labels produced using the game were indeed useful descriptions of the images. The results of searching for randomly chosen keywords were presented and show that the proportion of appropriate images when searching using the labels generated by the game is
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The first run of the ESP game used a collection of 350,000 images chosen by the developers. Later versions selected images at random from the web, using a small amount of filtering. Such images are reintroduced into the game several times until they are fully labeled. The random images were chosen
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Furthermore, a label is only stored after a certain number of players (N) have agreed on it. At this point, all of the taboo lists for the images are deleted and the image is returned to the game pool as if it were a fresh image. If X is the probability of a label being incorrect despite a player
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The ESP Game as it is currently implemented encourages players to assign "obvious" labels, which are most likely to lead to an agreement with the partner. But these labels can often be deduced from the labels already present using an appropriate language model and such labels therefore add only
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was historically a task that was difficult for computers to perform independently. Humans are perfectly capable of it, but are not necessarily willing. By making the recognition task a "game", people are more likely to participate. When questioned about how much they enjoyed playing the game,
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possible words, and once a word is entered by both partners (not necessarily at the same time), that word is agreed upon, and that word becomes a label for the image. Once they agree on a word, they are shown another image. They have two and a half minutes to label 15 images.
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Both partners have the option to pass; that is, give up on an image. Once one partner passes, the other partner is shown a message that their partner wishes to pass. Both partners must pass for a new image to be shown.
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little information to the system. A Microsoft research project assigns probabilities to the next label to be added. This model is then used in a program, which plays the ESP game without looking at the image.
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The choice of images used by the ESP game makes a difference in the player's experience. The game would be less entertaining if all the images were chosen from a single site and were all extremely similar.
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with a
Purpose (teleological social machine), providing an example of an intelligent system emerging from the interaction of human participants in the book "The shortcut" by
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extremely high. Further evaluation was achieved by comparing the labels generated using the game to labels generated by participants that were asked to describe the images.
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the only thing the two partners have in common is that they both see the same image, they must enter reasonable labels to have any chance of agreeing on one.
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126:("game with a purpose"), with a new user interface. Some other games that were also created by Luis von Ahn, such as "Peekaboom" and "
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70:) in 2006 in order to return better search results for its online images. The license of the data acquired by Ahn's
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having successfully labelled test images, then after N repetitions the probability of corruption is
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humans). This is necessary if there are an odd number of people playing the game.
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Google Tech Talk on Human
Computation by creator Luis von Ahn
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The shortcut: why intelligent machines do not think like us
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bought a license to create its own version of the game (
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developed to address the problem of creating difficult
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Google Tech Talk on human computation by Luis von Ahn
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301:: CS1 maint: location missing publisher (
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38:. The idea behind the game is to use the
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219:"Solving the web's image problem"
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