Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Labeling images with a computer game
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Introducing Game Theory and its Applications
Introducing Game Theory and its Applications
Peekaboom: a game for locating objects in images
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Verbosity: a game for collecting common-sense facts
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Tagging with hidden Markov models using ambiguous tags
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
KissKissBan: a competitive human computation game for image annotation
Proceedings of the ACM SIGKDD Workshop on Human Computation
Incentive cooperation strategies for peer-to-peer live multimedia streaming social networks
IEEE Transactions on Multimedia - Special section on communities and media computing
Combining web, mobile phones and public displays in large-scale: manhattan story mashup
PERVASIVE'07 Proceedings of the 5th international conference on Pervasive computing
IEEE Transactions on Circuits and Systems for Video Technology
Automatic image semantic interpretation using social action and tagging data
Multimedia Tools and Applications
HCII'11 Proceedings of the 14th international conference on Human-computer interaction: design and development approaches - Volume Part I
Efficient annotation of image data sets for computer vision applications
Proceedings of the 1st International Workshop on Visual Interfaces for Ground Truth Collection in Computer Vision Applications
Assistive tagging: A survey of multimedia tagging with human-computer joint exploration
ACM Computing Surveys (CSUR)
Semantics Discovery via Human Computation Games
International Journal on Semantic Web & Information Systems
Human computation: Image metadata acquisition based on a single-player annotation game
International Journal of Human-Computer Studies
| Hi-index | 0.00 |
Image indexing is one of the most difficult challenges facing the computer vision community. Addressing this issue, we designed an innovative approach to obtain an accurate label for images by taking into account the social aspects of human-based computation. The proposed approach is highly discriminative in comparison to an ordinary content-based image retrieval (CBIR) paradigm. It aims at what millions of individual gamers are enthusiastic to do, to enjoy themselves within a social competitive environment. It is achieved by setting the focus of the system on the social aspects of the gaming environment, which involves a widely distributed network of human players. Furthermore, this framework integrates a number of different algorithms that are commonly found in image processing and game theoretic approaches to obtain an accurate label. As a result, the framework is able to assign (or derive) accurate tags for images by eliminating annotations made by a less-rational (cheater) player. The performance analysis of this framework has been evaluated with a group of 10 game players. The result shows that the proposed approach is capable of obtaining a good annotation through a small number of game players.