Fetching the paper…
Reading the bibliography…
In the area of human fixation prediction, dozens of computational saliency models are proposed to reveal certain saliency characteristics under different assumptions and definitions.
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, no. 11, pp. 1254–1259, 1998
1998
Earlier work this paper cites.
Y. Rubner, C. Tomasi, and L. J. Guibas, “The earth mover’s distance as a metric for image retrieval,” International journal of computer vision , vol. 40, no. 2, pp. 99–121, 2000
2000
Earlier work this paper cites.
U. Rajashekar, L. K. Cormack, and A. C. Bovik, “Point-of-gaze analysis reveals visual search strategies,” in Human Vision and Electronic Imaging , vol. 5292, 2004, pp. 296–306
2004
Earlier work this paper cites.
Y. Hu, D. Rajan, and L.-T. Chia, “Adaptive local context suppression of multiple cues for salient visual attention detection,” in IEEE International Conference on Multimedia and Expo (ICME) , 2005
2005
Earlier work this paper cites.
N. D. Bruce and J. K. Tsotsos, “Saliency based on information maximization,” in Advances in Neural Information Processing Systems (NIPS) , Vancouver, BC, Canada, 2005, pp. 155–162
2005
Earlier work this paper cites.
R. J. Peters, A. Iyer, L. Itti, and C. Koch, “Components of bottom-up gaze allocation in natural images,” Vision research , vol. 45, no. 18, pp. 2397–2416, 2005
2005
Earlier work this paper cites.
B. W. Tatler, R. J. Baddeley, and I. D. Gilchrist, “Visual correlates of fixation selection: effects of scale and time,” Vision Research , vol. 45, no. 5, pp. 643–659, 2005
2005
Earlier work this paper cites.
J. Harel, C. Koch, and P. Perona, “Graph-based visual saliency,” in Advances in Neural Information Processing Systems (NIPS) , 2007, pp. 545–552
2007
Earlier work this paper cites.
L. Zhang, M. H. Tong, T. K. Marks, H. Shan, and G. W. Cottrell, “Sun: A Bayesian framework for saliency using natural statistics,” Journal of Vision , vol. 8, no. 7, pp. 32, 1–20, 2008
2008
Earlier work this paper cites.
P.-H. Tseng, R. Carmi, I. G. M. Cameron, D. P. Munoz, and L. Itti, “Quantifying center bias of observers in free viewing of dynamic natural scenes,” Journal of Vision , vol. 9, no. 7, pp. 4, 1–16, 2009
2009
Earlier work this paper cites.
T. Judd, K. Ehinger, F. Durand, and A. Torralba, “Learning to predict where humans look,” in IEEE International Conference on Computer Vision (ICCV) , 2009, pp. 2106–2113
2009
Earlier work this paper cites.
R. J. Peters and L. Itti, “Congruence between model and human attention reveals unique signatures of critical visual events,” in Advances in Neural Information Processing Systems (NIPS) , Vancouver, BC, Canada, 2009
2009
Earlier work this paper cites.
S. Goferman, L. Zelnik-Manor, and A. Tal, “Context-aware saliency detection,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2010, pp. 2376–2383
2010
Earlier work this paper cites.
V. Mahadevan and N. Vasconcelos, “Spatiotemporal saliency in dynamic scenes,” IEEE transactions on pattern analysis and machine intelligence , vol. 32, no. 1, pp. 171–177, 2010
2010
Earlier work this paper cites.
J. Li, Y. Tian, T. Huang, and W. Gao, “Probabilistic multi-task learning for visual saliency estimation in video,” International Journal of Computer Vision , vol. 90, no. 2, pp. 150–165, 2010
2010
Earlier work this paper cites.
W. Wang, Y. Wang, Q. Huang, and W. Gao, “Measuring visual saliency by site entropy rate,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2010, pp. 2368–2375
2010
Earlier work this paper cites.
V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in ICML , 2010, pp. 807–814
2010
Earlier work this paper cites.
N. Wilming, T. Betz, T. C. Kietzmann, and P. König, “Measures and limits of models of fixation selection,” PloS one , vol. 6, no. 9, p. e24038, 2011
2011
Earlier work this paper cites.
N. Riche, M. Mancas, B. Gosselin, and T. Dutoit, “Rare: A new bottom-up saliency model,” in IEEE Conference on Image Processing (ICIP) , 2012, pp. 641–644
2012
Earlier work this paper cites.
A. Borji and L. Itti, “Exploiting local and global patch rarities for saliency detection,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2012, pp. 478–485
2012
Cited alongside, same era.
T. Judd, F. Durand, and A. Torralba, “A benchmark of computational models of saliency to predict human fixations,” 2012
2012
Cited alongside, same era.
X. Hou, J. Harel, and C. Koch, “Image signature: Highlighting sparse salient regions,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 34, no. 1, pp. 194–201, 2012
2012
Cited alongside, same era.
Q. Zhao and C. Koch, “Learning visual saliency by combining feature maps in a nonlinear manner using adaboost,” Journal of Vision , vol. 12, no. 6, pp. 22, 1–15, 2012
2012
Cited alongside, same era.
J. Li, D. Xu, and W. Gao, “Removing label ambiguity in learning-based visual saliency estimation,” IEEE Transactions on Image Processing , vol. 21, no. 4, pp. 1513–1525, 2012
2014
Later among the works it cites.
J. Li, Y. Tian, and T. Huang, “Visual saliency with statistical priors,” International Journal of Computer Vision , vol. 107, no. 3, pp. 239–253, 2014
2014
Later among the works it cites.
A. F. Russell, S. Mihalaş, R. von der Heydt, E. Niebur, and R. Etienne-Cummings, “A model of proto-object based saliency,” Vision Research , vol. 94, pp. 1–15, 2014
2014
Later among the works it cites.
S. Lu, C. Tan, and J. Lim, “Robust and efficient saliency modeling from image co-occurrence histograms,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 36, no. 1, pp. 195–201, 2014
2014
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2012
Cited alongside, same era.
A. Borji, “Boosting bottom-up and top-down visual features for saliency estimation,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2012, pp. 438–445
2012
Cited alongside, same era.
C. Lang, G. Liu, J. Yu, and S. Yan, “Saliency detection by multitask sparsity pursuit,” IEEE Transactions on Image Processing , vol. 21, no. 3, pp. 1327–1338, 2012
2012
Cited alongside, same era.
S. Goferman, L. Zelnik-Manor, and A. Tal, “Context-aware saliency detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 34, no. 10, pp. 1915–1926, 2012
2012
Cited alongside, same era.
W. Hou, X. Gao, D. Tao, and X. Li, “Visual saliency detection using information divergence,” Pattern Recognition , vol. 46, no. 10, pp. 2658 – 2669, 2013
2013
Cited alongside, same era.
J. Zhang and S. Sclaroff, “Saliency detection: A boolean map approach,” in IEEE International Conference on Computer Vision (ICCV) , 2013, pp. 153–160
2013
Cited alongside, same era.
A. Borji and L. Itti, “State-of-the-art in visual attention modeling,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 35, no. 1, pp. 185–207, 2013
2013
Cited alongside, same era.
A. Borji, D. Sihite, and L. Itti, “Quantitative analysis of human-model agreement in visual saliency modeling: A comparative study,” IEEE Transactions on Image Processing , vol. 22, no. 1, pp. 55–69, 2013
2013
Cited alongside, same era.
R. Margolin, L. Zelnik-Manor, and A. Tal, “How to evaluate foreground maps?” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2014
2014
Later among the works it cites.
2014
Later among the works it cites.
2014
Later among the works it cites.
2014
Later among the works it cites.
J. Li, C. Xia, Y. Song, S. Fang, and X. Chen, “A data-driven metric for comprehensive evaluation of saliency models,” in IEEE International Conference on Computer Vision (ICCV) , 2015, pp. 190–198
2015
Later among the works it cites.
N. D. Bruce, C. Wloka, N. Frosst, S. Rahman, and J. K. Tsotsos, “On computational modeling of visual saliency: Examining what¡¯s right, and what¡¯s left,” Vision research , vol. 116, pp. 95–112, 2015
2015
Later among the works it cites.
J. Li, L.-Y. Duan, X. Chen, T. Huang, and Y. Tian, “Finding the secret of image saliency in the frequency domain,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 37, no. 12, pp. 2428–2440, 2015
2015
Later among the works it cites.
M. Kuemmerer, T. Wallis, and M. Bethge, “Information-theoretic model comparison unifies saliency metrics,” Proceedings of the National Academy of Science , vol. 112, no. 52, pp. 16 054–16 059, Oct 2015. [Online]. Available: http://www.pnas.org/content/112/52/16054.abstract
2015
Later among the works it cites.
M. Kümmerer, T. S. Wallis, and M. Bethge, “Information-theoretic model comparison unifies saliency metrics,” Proceedings of the National Academy of Sciences , vol. 112, no. 52, pp. 16 054–16 059, 2015
2015
Later among the works it cites.
J. Li, Y. Tian, X. Chen, and T. Huang, “Measuring visual surprise jointly from intrinsic and extrinsic contexts for image saliency estimation,” International Journal of Computer Vision , pp. 1–17, 2016
2016
Later among the works it cites.
S. S. Kruthiventi, V. Gudisa, J. H. Dholakiya, and R. Venkatesh Babu, “Saliency unified: A deep architecture for simultaneous eye fixation prediction and salient object segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 5781–5790
2016
Later among the works it cites.
Z. Liu, J. Li, L. Ye, G. Sun, and L. Shen, “Saliency detection for unconstrained videos using superpixel-level graph and spatiotemporal propagation,” IEEE transactions on circuits and systems for video technology , 2016
2016
Later among the works it cites.
Y. Zhang, F. Zhang, and L. Guo, “Saliency detection by selective color features,” Neurocomputing , vol. 203, pp. 34–40, 2016
2016
Later among the works it cites.
A. Borji and J. Tanner, “Reconciling saliency and object center-bias hypotheses in explaining free-viewing fixations,” IEEE transactions on neural networks and learning systems , vol. 27, no. 6, pp. 1214–1226, 2016
2016
Later among the works it cites.