Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Computer Graphics and Applications
Telling humans and computers apart automatically
Communications of the ACM - Information cities
Labeling images with a computer game
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Peekaboom: a game for locating objects in images
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
Image and video matting: a survey
Foundations and Trends® in Computer Graphics and Vision
The MIR flickr retrieval evaluation
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
A Survey of Human Computation Systems
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
Image tag refinement towards low-rank, content-tag prior and error sparsity
Proceedings of the international conference on Multimedia
KissKissBan: a competitive human computation game for image annotation
ACM SIGKDD Explorations Newsletter
Recovering human body configurations: combining segmentation and recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Ask'nSeek: a new game for object detection and labeling
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
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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.