@conference {14902,
	title = {Odd Leaf Out: Improving Visual Recognition with Games},
	booktitle = {Privacy, security, risk and trust (passat), 2011 ieee third international conference on and 2011 ieee third international conference on social computing (socialcom)},
	year = {2011},
	month = {2011/10//},
	pages = {87 - 94},
	abstract = {A growing number of projects are solving complex computational and scientific tasks by soliciting human feedback through games. Many games with a purpose focus on generating textual tags for images. In contrast, we introduce a new game, Odd Leaf Out, which provides players with an enjoyable and educational game that serves the purpose of identifying misclassification errors in a large database of labeled leaf images. The game uses a novel mechanism to solicit useful information from players{\textquoteright} incorrect answers. A study of 165 players showed that game data can be used to identify mislabeled leaves much more quickly than would have been possible using a computer vision algorithm alone. Domain novices and experts were equally good at identifying mislabeled images, although domain experts enjoyed the game more. We discuss the successes and challenges of this new game, which can be applied to other domains with labeled image datasets.},
	keywords = {algorithm;educational, classification;object, computational, computing;botany;computer, datasets;misclassification, errors;scientific, feedback;labeled, game;human, games;computer, games;image, image, leaf, Odd, Out;complex, recognition;, recognition;biology, tags;visual, tasks;computer, tasks;textual, VISION},
	doi = {10.1109/PASSAT/SocialCom.2011.225},
	author = {Hansen,D. L and Jacobs, David W. and Lewis,D. and Biswas,A. and Preece,J. and Rotman,D. and Stevens,E.}
}
@conference {13148,
	title = {Objects in Action: An Approach for Combining Action Understanding and Object Perception},
	booktitle = {Computer Vision and Pattern Recognition, 2007. CVPR {\textquoteright}07. IEEE Conference on},
	year = {2007},
	month = {2007/06//},
	pages = {1 - 8},
	abstract = {Analysis of videos of human-object interactions involves understanding human movements, locating and recognizing objects and observing the effects of human movements on those objects. While each of these can be conducted independently, recognition improves when interactions between these elements are considered. Motivated by psychological studies of human perception, we present a Bayesian approach which unifies the inference processes involved in object classification and localization, action understanding and perception of object reaction. Traditional approaches for object classification and action understanding have relied on shape features and movement analysis respectively. By placing object classification and localization in a video interpretation framework, we can localize and classify objects which are either hard to localize due to clutter or hard to recognize due to lack of discriminative features. Similarly, by applying context on human movements from the objects on which these movements impinge and the effects of these movements, we can segment and recognize actions which are either too subtle to perceive or too hard to recognize using motion features alone.},
	keywords = {analysis;image, approach;action, Bayesian, classification;image, classification;object, framework;Bayes, interactions;inference, interpretation, localization;object, methods;gesture, MOTION, movements;human, perception;, perception;human-object, perception;object, process;object, processing;visual, recognition;image, recognition;video, segmentation;action, segmentation;object, signal, understanding;human},
	doi = {10.1109/CVPR.2007.383331},
	author = {Gupta,A. and Davis, Larry S.}
}
