A. W. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain, Content-based image retrieval at the end of the early years, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.12, pp.1349-1380, 2000.
DOI : 10.1109/34.895972

A. Jaimes, M. Christel, S. Gilles, R. Sarukkai, and W. Ma, Multimedia information retrieval, Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval , MIR '05, pp.3-8, 2005.
DOI : 10.1145/1101826.1101829

R. Datta, D. Joshi, J. Li, and J. Z. Wang, Image retrieval, ACM Computing Surveys, vol.40, issue.2, pp.1-60, 2008.
DOI : 10.1145/1348246.1348248

K. Athanasakos, V. Stathopoulos, and J. Jose, A Framework for Evaluating Automatic Image Annotation Algorithms Advances in Information Retrieval, LNCS, vol.5993, pp.217-228, 2010.

P. Zhang, Z. Zhang, M. Li, W. Y. Ma, and H. J. Zhang, A probabilistic semantic model for image annotation and multi-modal image retrieval, Multimedia Systems, vol.22, issue.1, pp.27-33, 2006.
DOI : 10.1007/s00530-006-0025-1

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

T. Volker, A. Thom, and S. M. Tahaghoghi, Modeling Human Judgment of Digital Imagery for Multimedia Retrieval, IEEE Transactions on Multimedia, vol.9, issue.5, pp.967-974, 2007.
DOI : 10.1109/TMM.2007.900153

J. Howe, Crowdsourcing: Why the Power of the Crowd Is Driving the Future of Business, 2008.

G. Kazai, J. Kamps, M. Koolen, and N. Milic-frayling, Crowdsourcing for book search evaluation, Proceedings of the 34th international ACM SIGIR conference on Research and development in Information, SIGIR '11, 2011.
DOI : 10.1145/2009916.2009947

A. Mechanical-turk-eickhoff, C. De-vries, and A. P. , How Crowdsourcable is Your Task, Proc. of the Workshop on Crowdsourcing for Search and Data Mining, 2011.

O. Ludwig, D. Delgado, V. Goncalves, and U. Nunes, Trainable Classifier-Fusion Schemes: An Application To Pedestrian Detection, Proc. of 12th International IEEE Conference On Intelligent Transportation Systems, pp.432-437, 2009.

I. H. Witten and E. Frank, Data mining, ACM SIGMOD Record, vol.31, issue.1, 2005.
DOI : 10.1145/507338.507355

Z. Theodosiou, A. Kounoudes, N. Tsapatsoulis, and M. Milis, MuLVAT: A Video Annotation Tool Based on XML-Dictionaries and Shot Clustering, Proc. of the 19th International Conference on Artificial Neural Networks: Part II, pp.913-922, 2009.
DOI : 10.1007/978-3-642-04277-5_92

D. M. Tax and R. P. Duin, Using two-class classifiers for multiclass classification, Object recognition supported by user interaction for service robots, pp.124-127, 2002.
DOI : 10.1109/ICPR.2002.1048253

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

J. Platt, Fast Training of Support Vector Machines using Sequential Minimal Optimization Advances in Kernel Methods-Support Vector Learning, 1998.

R. Fan, P. Chen, and C. Lin, Working set selection using the second order information for training SVM, Journal of Machine Learning Research, vol.6, pp.1889-1918, 2005.

L. Breiman, Random Forests, Machine Learning, vol.45, issue.1, pp.5-32, 2001.
DOI : 10.1023/A:1010933404324

N. Landwehr, M. Hall, and E. Frank, Logistic Model Trees, Machine Learning, vol.4, issue.3, pp.161-205, 2005.
DOI : 10.1007/s10994-005-0466-3