Rethinking the ESP Game  The ESP Game (Ahn and Dabbish 2004) was
designed to harvest human intelligence to assign labels to
images - a task which is still difficult for even the most
advanced systems in image processing. However, 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 little information to the system.
We present a language model which, given enough instances of
labeled images as training data, can assign 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. Even
without any understanding of the actual image, the program
manages to agree with the randomly assigned human partner on a
label for 69% of all images, and for 81% of images which have at
least one "off-limits" term a