Peekaboom: a game for locating objects in images
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Designing games with a purpose
Communications of the ACM - Designing games with a purpose
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
KissKissBan: a competitive human computation game for image annotation
ACM SIGKDD Explorations Newsletter
Purposive hidden-object-game: embedding human computation in popular game
MM '11 Proceedings of the 19th ACM international conference on Multimedia
How well do you know Tom Hanks?: using a game to learn about face recognition
CHI '13 Extended Abstracts on Human Factors in Computing Systems
Crowdsourced object segmentation with a game
Proceedings of the 2nd ACM international workshop on Crowdsourcing for multimedia
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This paper proposes a novel approach to detect and label objects within images and describes a two-player web-based guessing game --- Ask'nSeek --- that supports these tasks in a fun and interactive way. Ask'nSeek asks users to guess the location of a hidden region within an image with the help of semantic and topological clues. The information collected from game logs is combined with results from content analysis algorithms and used to feed a machine learning algorithm that outputs the outline of the most relevant regions within the image and their names. Two noteworthy aspects of the proposed game are: (i) it solves two computer vision problems --- object detection and labeling --- in a single game; and (ii) it learns spatial relations within the image from game logs. The game has been evaluated through user studies, which confirmed that it was easy to understand, intuitive, and fun to play.