purposive hidden-object game (p-hog) towards imperceptible human computation
This demonstration presents a novel purposive web game on iOS to collect object locations, named Purposive Hidden-Object-Game(P-HOG). Having sufficient training images with known object locations is crucial for many multimedia tasks. P-HOG imperceptibly embeds localizing objects into the gaming process so that P-HOG preserves attractiveness to common players. During the game, players need to localize both automatically inserted known items and unknown objects (which are aimed to localize) to gain scores. The unknown objects are indicated by the refined user-provided tags from photo sharing websites. The difficulty mainly lies in how to insert known items naturally and thus preserve the game's playability. The P-HOG can be applied for constructing a large database, which contains located objects and may benefit general learning-based algorithms for multimedia tasks. The comprehensive experiments shows that P-HOG appeals to general players and can easily perform quality control, and hence effective for collecting massive object locations with high accuracy.
human computing gwap hidden object game