KissKissBan: a competitive human computation game for image annotation

  • Authors:

  • Chien-Ju Ho;Tao-Hsuan Chang;Jong-Chuan Lee;Jane Yung-jen Hsu;Kuan-Ta Chen

  • Affiliations:

  • Institute of Information Science;National Taiwan University;National Taiwan University;National Taiwan University;Institute of Information Science

  • Venue:
  • Proceedings of the ACM SIGKDD Workshop on Human Computation
  • Year:
  • 2009

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Abstract

In this paper, we propose a competitive human computation game, KissKissBan (KKB), for image annotation. KKB is different from other human computation games since it integrates both collaborative and competitive elements in the game design. In a KKB game, one player, the blocker, competes with the other two collaborative players, the couples; while the couples try to find consensual descriptions about an image, the blocker's mission is to prevent the couples from reaching consensus. Because of its design, KKB possesses two nice properties over the traditional human computation game. First, since the blocker is encouraged to stop the couples from reaching consensual descriptions, he will try to detect and prevent coalition between the couples; therefore, these efforts naturally form a player-level cheating-proof mechanism. Second, to evade the restrictions set by the blocker, the couples would endeavor to bring up a more diverse set of image annotations. Experiments hosted on Amazon Mechanical Turk and a gameplay survey involving 17 participants have shown that KKB is a fun and efficient game for collecting diverse image annotations.