A. Baranès and P. Oudeyer, R-iac: Robust intrinsically motivated exploration and active learning. Autonomous Mental Development, IEEE Transactions on, vol.1, issue.3, pp.155-169, 2009.

M. Björkman and D. Kragic, Active 3D scene segmentation and detection of unknown objects, 2010 IEEE International Conference on Robotics and Automation, pp.3114-3120, 2010.
DOI : 10.1109/ROBOT.2010.5509973

A. Boduroglu, P. Shah, E. Richard, and . Nisbett, Cultural Differences in Allocation of Attention in Visual Information Processing, Journal of Cross-Cultural Psychology, vol.40, issue.3, pp.349-360, 2009.
DOI : 10.1177/0022022108331005

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

Z. Bylinskii, T. Judd, F. Durand, A. Oliva, and A. Torralba, Mit saliency benchmark

M. José, M. Cañas, T. Martínez-de-la-casa, and . González, An overt visual attention mechanism based on saliency dynamics, International Journal of Intelligent Computing in Medical Sciences & Image Processing, vol.2, issue.2, pp.93-100, 2008.

E. Erdem and A. Erdem, Visual saliency estimation by nonlinearly integrating features using region covariances, Journal of Vision, vol.13, issue.4, p.11, 2013.
DOI : 10.1167/13.4.11
URL : http://doi.org/10.1167/13.4.11

A. Martin, . Fischler, C. Robert, and . Bolles, Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography, Communications of the ACM, vol.24, issue.6, pp.381-395, 1981.

H. Fred and . Hamker, The emergence of attention by population-based inference and its role in distributed processing and cognitive control of vision, Computer Vision and Image Understanding, vol.100, issue.1, pp.64-106, 2005.

J. Harel, C. Koch, and P. Perona, Graph-based visual saliency, Advances in neural information processing systems, pp.545-552, 2006.

M. Harter and L. Anllo-vento, Visual-spatial attention: preparation and selection in children and adults, Electroencephalography and clinical neurophysiology. Supplement, vol.42, pp.183-194, 1990.

X. Huang and J. Weng, Novelty and reinforcement learning in the value system of developmental robots, 2002.

L. Itti, C. Koch, and E. Niebur, A model of saliency-based visual attention for rapid scene analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.20, issue.11, pp.1254-1259, 1998.
DOI : 10.1109/34.730558

K. Lai, L. Bo, X. Ren, and D. Fox, A large-scale hierarchical multi-view RGB-D object dataset, 2011 IEEE International Conference on Robotics and Automation, pp.1817-1824, 2011.
DOI : 10.1109/ICRA.2011.5980382

S. Minut and S. Mahadevan, A reinforcement learning model of selective visual attention, Proceedings of the fifth international conference on Autonomous agents , AGENTS '01, pp.457-464, 2001.
DOI : 10.1145/375735.376414

P. Oudeyer, F. Kaplan, and V. V. Hafner, Intrinsic Motivation Systems for Autonomous Mental Development, IEEE Transactions on Evolutionary Computation, vol.11, issue.2, pp.265-286, 2007.
DOI : 10.1109/TEVC.2006.890271

H. Peng, B. Li, W. Xiong, W. Hu, and R. Ji, RGBD Salient Object Detection: A Benchmark and Algorithms, Computer Vision?ECCV 2014, pp.92-109, 2014.
DOI : 10.1007/978-3-319-10578-9_7

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh et al., Imagenet large scale visual recognition challenge. arXiv preprint, 2014.

A. Saxena, H. Sung, . Chung, Y. Andrew, and . Ng, Learning depth from single monocular images, Advances in Neural Information Processing Systems, pp.1161-1168, 2005.

M. Van-den-bergh, X. Boix, G. Roig, L. Benjamin-de-capitani, and . Van-gool, Seeds: Superpixels extracted via energy-driven sampling, Computer Vision?ECCV 2012, pp.13-26, 2012.

J. Zhang and S. Sclaroff, Saliency Detection: A Boolean Map Approach, 2013 IEEE International Conference on Computer Vision, pp.153-160, 2013.
DOI : 10.1109/ICCV.2013.26

Q. Zhao and C. Koch, Learning a saliency map using fixated locations in natural scenes, Journal of Vision, vol.11, issue.3, p.9, 2011.
DOI : 10.1167/11.3.9

J. Zhu, J. Wu, Y. Wei, E. Chang, and Z. Tu, Unsupervised Object Class Discovery via Saliency-Guided Multiple Class Learning, Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pp.3218-3225, 2012.
DOI : 10.1109/TPAMI.2014.2353617