Optimizing visual attention models for predicting human fixations using Genetic Algorithms

2013 IEEE Congress on Evolutionary Computation, CEC 2013

Volumn , Issue , 2013, Pages 1302-1309

  Naqvi, Syed S ^a   Browne, Will N ^a   Hollitt, Christopher ^a  

^a VICTORIA UNIVERSITY OF WELLINGTON   (New Zealand)

Author keywords

[No Author keywords available]

Indexed keywords

BOTTOM-UP VISUAL ATTENTIONS; OPTIMAL PERFORMANCE; PERFORMANCE COMPARISON; PREDICTION PERFORMANCE; RECEIVER OPERATING CHARACTERISTIC CURVES; STANDARD PERFORMANCE; VISUAL ATTENTION MODEL; VISUAL SALIENCY MODEL;

ANIMATION; FORECASTING; OPTIMIZATION; VISUALIZATION;

GENETIC ALGORITHMS;

EID: 84881602215     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CEC.2013.6557715     Document Type: Conference Paper

References (27)

1. Q. Zhao and C. Koch, "Learning a saliency map using fixated locations in natural scenes." Journal of Vision, 2011, vol. 11, no. 3, pp. 1-15.
2. A. Santella, M. Agrawala, D. Decarlo, D. Salesin, and M. Cohen, "Gaze-based interaction for semi-automatic photo cropping," in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2006, pp. 771-780.
3. Z. Wang, L. Lu, and A. C. Bovik, "Foveation scalable video coding with automatic fixation selection," IEEE Transactions on Image Processing, vol. 12, pp. 243-254, 2003.
4. B. Ali and I. Laurent, "State-of-the-art in visual attention modeling." IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 1, pp. 185-207, 2013.
5. J. Tilke, D. FRedo, and T. Antonio, "A benchmark of computational models of saliency to predict human fixations," MIT Computer Science and Artificial Intelligence Lab (CSAIL), Tech. Rep. MIT-CSAIL-TR-2012-001, January 2012.
6. L. Itti, C. Koch, and E. Niebur, "A model of saliency-based visual attention for rapid scene analysis," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 20, no. 11, pp. 1254-1259, nov 1998.
7. A. Torralba, "Modeling global scene factors in attention." Journal of the Optical Society of America. A, Optics, image science, and vision, vol. 20, no. 7, pp. 1407-1418, 2003.
8. D. B. B. Neil and J. K. Tsotsos, "Saliency, attention, and visual search: An information theoretic approach," Journal of Vision, vol. 9, no. 3, pp. 1-24, 2009.
9. R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk, "Frequency- tuned salient region detection," in IEEE Conference on Computer Vision and Pattern Recognition (CVPR09). IEEE Computer Society, 2009, pp. 1597-1604.
10. W. Kienzle, F. A. Wichmann, B. Schlkopf, and M. O. Franz, "A nonparametric approach to bottom-up visual saliency," in Proc. NIPS 19, 2006, pp. 689-696.
11. H. J. Seo and M. P., "Nonparametric bottom-up saliency detection by self-resemblance," in Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on, 2009, pp. 45-52.
12. X. Hou and L. Zhang, "Saliency detection: A spectral residual approach," in IEEE Conference on Computer Vision and Pattern Recognition (CVPR07). IEEE Computer Society, 2007, pp. 1-8.
13. H. J. Seo and M. P., "Static and space-time visual saliency detection by self-resemblance." Journal of vision, vol. 9, no. 12, pp. 1-27, 2009.
14. K. Deep, K. P. Singh, M. L. Kansal, and C. Mohan, "A real coded genetic algorithm for solving integer and mixed integer optimization problems," Applied Mathematics and Computation, vol. 212, no. 2, pp. 505-518, 2009.
15. D. Walther and C. Koch, "Modeling attention to salient proto-objects," Neural Networks, vol. 19, no. 9, pp. 1395-1407, 2006.
16. L. Itti and C. Koch, "A comparison of feature combination strategies for saliency-based visual attention systems," Journal of Electronic Imaging, vol. 10, no. 1, pp. 161-169, 1999.
17. S. Leyffer, J. Linderoth, J. Luedtke, A. Miller, and T. Munson, "Applications and algorithms for mixed integer nonlinear programming," Journal of Physics: Conference Series, vol. 180, no. 1, pp. 1-5, 2009.
18. J. Li, L. M.D., X. An, X. Xu, and H. He, "Visual saliency based on scale-space analysis in the frequency domain," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 6, no. 99, pp. 1-16, 2012.
19. R. J. Peters, A. Iyer, L. Itti, and C. Koch, "Components of bottom-up gaze allocation in natural images," Vision Research, vol. 45, no. 8, pp. 2397-2416, 2005.
20. B. Peng and Z. Liming, "Visual saliency: a biologically plausible contourlet-like frequency domain approach," Cognitive Neurodynamics, vol. 4, pp. 189-198, 2010.
21. M. Wischnewski, J. J. Steil, L. Kehrer, and W. X. Schneider, "Integrating inhomogeneous processing and proto-object formation in a computational model of visual attention," Cognitive Systems Monograph, vol. 6, pp. 93-102, 2009.
22. K. Deep and M. Thakur, "A new crossover operator for real coded genetic algorithms," Applied Mathematics and Computation, vol. 188, no. 1, pp. 895-911, 2007.
23. -, "A new mutation operator for real coded genetic algorithms," Applied Mathematics and Computation, vol. 193, no. 1, pp. 211-230, 2007.
24. N. Riche, M. Mancas, B. Gosselin, and T. Dutoit, "RARE: a New Bottom-Up Saliency Mode," in Proceedings of the International Conference on Image Processing (IEEE ICIP), 2012.
25. A. Torralba, A. Oliva, M. Castelhano, and J. Henderson, "Contextual guidance of eye movements and attention in real-world scenes: the role of global features in object search." Psychological Review, vol. 113, no. 4, pp. 766-786, 2006.
26. 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, pp. 32-32, 2008.
27. R. Braithwaite and B. Bhanu, "Hierarchical gabor filters for object detection in infrared images," in IEEE Conference on Computer Vision and Pattern Recognition (CVPR94). IEEE Computer Society, 1994, pp. 628-631.