Purposive hidden-object-game: Embedding human computation in popular game

Abstract

Having sufficient training images with known locations of objects is critical for modern image annotation, image retrieval, and object detection tasks. However, it is typically extremely labor-intensive to collect such data, as the process usually involves tedious manual-cropping and hand-labeling. In this work, following the principle of games with a purpose (GWAP), we design a so-called purposive hidden-object-game (P-HOG), which seamlessly embeds object localization into the enjoyable HOG gaming process. As indicated by its large number of online players and downloads, HOG is very popular and P-HOG thus possesses great potentials in aggregating massive informative annotations. For each gaming image, besides identifying the known hidden objects inserted automatically towards semantic and visual naturalness, players also imperceptibly locate the spatial positions of the unknown objects, which are indicated by the refined user-provided tags from Flickr.com or other photo sharing websites. We conduct a pilot study of the game prototype and the comprehensive experiments show that the P-HOG appeals to general players, and is effective for collecting massive object locations with satisfying accuracy. Copyright 2011 ACM.