We introduce an interactive framework for image
understanding, a game that is enjoyable and provide valuable
image annotations. When people play the game, they provide
useful information about contents of an image. In reality the
most accurate method to describe the content of an image is
manual labelling. Our approach is to motivate people to label
imagers while entertaining themselves. Therefore if this game
becomes popular it will be able to annotate most imagers on
the web within a couple of months. When considering
accuracy we use a combination of computer vision techniques
to secure the accuracy of image labelling. By doing this we
believe our system will make a significant contribution to
address the semantic gap in the computer vision sector.
%0 Conference Paper
%1 seneviratne2008image
%A Seneviratne, Lasantha
%A Izquierdo, Ebroul
%B Proceedings of the 2nd K-Space PhD Jamboree Workshop
%D 2008
%E Simone, Francesca De
%E Nemrava, Jan
%E Bailer, Werner
%I CEUR-WS
%K annotation game hpi image-libraries ss2011 taggig ugm
%T Image Annotation Through Gaming
%U http://ftp.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-379/paper8.pdf
%X We introduce an interactive framework for image
understanding, a game that is enjoyable and provide valuable
image annotations. When people play the game, they provide
useful information about contents of an image. In reality the
most accurate method to describe the content of an image is
manual labelling. Our approach is to motivate people to label
imagers while entertaining themselves. Therefore if this game
becomes popular it will be able to annotate most imagers on
the web within a couple of months. When considering
accuracy we use a combination of computer vision techniques
to secure the accuracy of image labelling. By doing this we
believe our system will make a significant contribution to
address the semantic gap in the computer vision sector.
@inproceedings{seneviratne2008image,
abstract = {We introduce an interactive framework for image
understanding, a game that is enjoyable and provide valuable
image annotations. When people play the game, they provide
useful information about contents of an image. In reality the
most accurate method to describe the content of an image is
manual labelling. Our approach is to motivate people to label
imagers while entertaining themselves. Therefore if this game
becomes popular it will be able to annotate most imagers on
the web within a couple of months. When considering
accuracy we use a combination of computer vision techniques
to secure the accuracy of image labelling. By doing this we
believe our system will make a significant contribution to
address the semantic gap in the computer vision sector.
},
added-at = {2011-04-11T11:42:17.000+0200},
author = {Seneviratne, Lasantha and Izquierdo, Ebroul},
biburl = {https://www.bibsonomy.org/bibtex/2f965d96aa5122ca1b4cbeb739bb449e2/datentaste},
booktitle = {Proceedings of the 2nd K-Space PhD Jamboree Workshop},
editor = {Simone, Francesca De and Nemrava, Jan and Bailer, Werner},
interhash = {5f5d2b0b5a3127737c82241debf59218},
intrahash = {f965d96aa5122ca1b4cbeb739bb449e2},
keywords = {annotation game hpi image-libraries ss2011 taggig ugm},
publisher = {CEUR-WS},
timestamp = {2011-04-11T11:42:17.000+0200},
title = {Image Annotation Through Gaming},
url = {http://ftp.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-379/paper8.pdf},
year = 2008
}