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The Scientific World Journal
Volume 2014, Article ID 137349, 16 pages
http://dx.doi.org/10.1155/2014/137349
Research Article

Saliency Detection Using Sparse and Nonlinear Feature Representation

1Beijing Key Laboratory of Intelligent Information Technology, School of Computer Science, Beijing Institute of Technology, Beijing 100081, China
2School of Automation, Beijing Institute of Technology, Beijing 100081, China
3Centres of Excellence in Science and Applied Technologies, Islamabad 44000, Pakistan

Received 17 February 2014; Accepted 11 March 2014; Published 8 May 2014

Academic Editor: Antonio Fernández-Caballero

Copyright © 2014 Shahzad Anwar et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. C. Siagian and L. Itti, “Biologically inspired mobile robot vision localization,” IEEE Transactions on Robotics, vol. 25, no. 4, pp. 861–873, 2009. View at Publisher · View at Google Scholar · View at Scopus
  2. J. Li, M. D. Levine, X. An, X. Xu, and H. He, “Visual saliency based on scale-space analysis in the frequency domain,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 4, pp. 996–1010, 2012. View at Google Scholar
  3. Y. Su, Q. Zhao, L. Zhao, and D. Gu, “Abrupt motion tracking using a visual saliency embedded particle filter,” Pattern Recognition, vol. 47, no. 5, pp. 1826–1834, 2014. View at Publisher · View at Google Scholar
  4. C. Guo, Q. Ma, and L. Zhang, “Spatio-temporal saliency detection using phase spectrum of quaternion fourier transform,” in Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR '08), pp. 1–8, June 2008. View at Publisher · View at Google Scholar · View at Scopus
  5. X. Hou and L. Zhang, “Thumbnail generation based on global saliency,” in Advances in Cognitive Neurodynamics—ICCN 2007, pp. 999–1003, Springer, Amsterdam, The Netherlands, 2007. View at Publisher · View at Google Scholar
  6. 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. View at Publisher · View at Google Scholar
  7. 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. View at Publisher · View at Google Scholar · View at Scopus
  8. A. M. Treisman, “Feature integration theory,” Cognitive Psychology, vol. 12, no. 1, pp. 97–136, 1980. View at Publisher · View at Google Scholar
  9. J. K. Tsotsos, S. M. Culhane, W. Y. Kei Wai, Y. Lai, N. Davis, and F. Nuflo, “Modeling visual attention via selective tuning,” Artificial Intelligence, vol. 78, no. 1-2, pp. 507–545, 1995. View at Google Scholar · View at Scopus
  10. R. Rae, Gestikbasierte Mensch-Maschine-Kommunikation auf der grundlage visueller aufmerksamkeit und adaptivität [Ph.D. thesis], Universität Bielefeld, 2000.
  11. D. Parkhurst, K. Law, and E. Niebur, “Modeling the role of salience in the allocation of overt visual attention,” Vision Research, vol. 42, no. 1, pp. 107–123, 2002. View at Publisher · View at Google Scholar · View at Scopus
  12. 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. View at Publisher · View at Google Scholar · View at Scopus
  13. M. Cerf, J. Harel, W. Einhauser, and C. Koch, “Predicting human gaze using low-level saliency combined with face detection,” in Advances in Neural Information Processing Systems, vol. 20, pp. 241–248, 2007. View at Google Scholar
  14. A. Torralba, “Modeling global scene factors in attention,” Journal of the Optical Society of America A: Optics and Image Science, and Vision, vol. 20, no. 7, pp. 1407–1418, 2003. View at Google Scholar · View at Scopus
  15. A. Oliva and A. Torralba, “Modeling the shape of the scene: a holistic representation of the spatial envelope,” International Journal of Computer Vision, vol. 42, no. 3, pp. 145–175, 2001. View at Publisher · View at Google Scholar · View at Scopus
  16. N. D. B. Bruce and J. K. Tsotsos, “Saliency, attention and visual search: an information theoretic approach,” Journal of Vision, vol. 9, no. 3, article 5, 2009. View at Publisher · View at Google Scholar · View at Scopus
  17. E. Erdem and A. Erdem, “Visual saliency estimation by nonlinearly integrating features using region covariances,” Journal of Vision, vol. 13, no. 4, article 11, 2013. View at Publisher · View at Google Scholar
  18. J. H. van Hateren, “Real and optimal neural images in early vision,” Nature, vol. 360, no. 6399, pp. 68–70, 1992. View at Publisher · View at Google Scholar · View at Scopus
  19. D. J. Field, “Relations between the statistics of natural images and the response properties of cortical cells,” Journal of the Optical Society of America A: Optics and Image Science, vol. 4, no. 12, pp. 2379–2394, 1987. View at Google Scholar · View at Scopus
  20. B. A. Olshausen and D. J. Field, “Natural image statistics and efficient coding,” Network: Computation in Neural Systems, vol. 7, no. 2, pp. 333–339, 1996. View at Google Scholar · View at Scopus
  21. M. Kwon, G. Legge, F. Fang, A. Cheong, and S. He, “Identifying the mechanism of adaptation to prolonged contrast reduction,” Journal of Vision, vol. 9, no. 8, p. 976, 2009. View at Google Scholar
  22. X. Sun, H. Yao, and R. Ji, “Visual attention modeling based on short-term environmental adaption,” Journal of Visual Communication and Image Representation, vol. 24, no. 2, pp. 171–180, 2013. View at Publisher · View at Google Scholar
  23. A. Garcia-Diaz, X. R. Fdez-Vidal, X. M. Pardo, and R. Dosil, “Saliency from hierarchical adaptation through decorrelation and variance normalization,” Image and Vision Computing, vol. 30, no. 1, pp. 51–64, 2012. View at Publisher · View at Google Scholar · View at Scopus
  24. C. E. Shannon, “A mathematical theory of communication,” Bell System Technical Journal, vol. 27, no. 3, pp. 379–423, 1948. View at Publisher · View at Google Scholar
  25. Y. Karklin and M. S. Lewicki, “Emergence of complex cell properties by learning to generalize in natural scenes,” Nature, vol. 457, no. 7225, pp. 83–86, 2009. View at Publisher · View at Google Scholar · View at Scopus
  26. H. J. Seo and P. Milanfar, “Static and space-time visual saliency detection by self-resemblance,” Journal of Vision, vol. 9, no. 12, pp. 1–27, 2009. View at Publisher · View at Google Scholar · View at Scopus
  27. D. J. Jobson, Z.-U. Rahman, and G. A. Woodell, “Properties and performance of a center/surround retinex,” IEEE Transactions on Image Processing, vol. 6, no. 3, pp. 451–462, 1997. View at Publisher · View at Google Scholar · View at Scopus
  28. A. Guo, D. Zhao, S. Liu, X. Fan, and W. Gao, “Visual attention based image quality assessment,” in Proceedings of the 18th IEEE International Conference on Image Processing (ICIP '11), pp. 3297–3300, September 2011. View at Publisher · View at Google Scholar · View at Scopus
  29. A. Borji and L. Itti, “Exploiting local and global patch rarities for saliency detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '12), pp. 478–485, 2012. View at Publisher · View at Google Scholar
  30. L. Duan, C. Wu, J. Miao, L. Qing, and Y. Fu, “Visual saliency detection by spatially weighted dissimilarity,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '11), pp. 473–480, June 2011. View at Publisher · View at Google Scholar · View at Scopus
  31. J. M. Wolfe, “Guided search 2.0 a revised model of visual search,” Psychonomic Bulletin & Review, vol. 1, no. 2, pp. 202–238, 1994. View at Publisher · View at Google Scholar
  32. O. Le Meur, P. Le Callet, D. Barba, and D. Thoreau, “A coherent computational approach to model bottom-up visual attention,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 5, pp. 802–817, 2006. View at Publisher · View at Google Scholar · View at Scopus
  33. A. Torralba, A. Oliva, M. S. Castelhano, and J. M. 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. View at Publisher · View at Google Scholar · View at Scopus
  34. G. Kootstra, N. Bergström, and D. Kragic, “Using symmetry to select fixation points for segmentation,” in Proceedings of the 20th International Conference on Pattern Recognition (ICPR '10), pp. 3894–3897, August 2010. View at Publisher · View at Google Scholar · View at Scopus
  35. C. Li, J. Xue, N. Zheng, X. Lan, and Z. Tian, “Spatio-temporal saliency perception via hypercomplex frequency spectral contrast,” Sensors, vol. 13, no. 3, pp. 3409–3431, 2013. View at Publisher · View at Google Scholar
  36. L. Itti and P. Baldi, “Bayesian surprise attracts human attention,” Vision Research, vol. 49, no. 10, pp. 1295–1306, 2009. View at Publisher · View at Google Scholar · View at Scopus
  37. J. Harel, C. Koch, and P. Perona, “Graph-based visual saliency,” in Advances in Neural Information Processing Systems, vol. 19, pp. 545–552, 2006. View at Google Scholar
  38. X. Hou and L. Zhang, “Saliency detection: a spectral residual approach,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '07), pp. 1–8, June 2007. View at Publisher · View at Google Scholar · View at Scopus
  39. B. Schauerte and R. Stiefelhagen, “Quaternion-based spectral saliency detection for eye fixation prediction,” in Computer Vision—ECCV 2012, A. Fitzgibbon, S. Lazebnik, P. Perona, Y. Sato, and C. Schmid, Eds., Lecture Notes in Computer Science, pp. 116–129, 2012. View at Google Scholar
  40. W. Kienzle, F. Wichmann, B. Schölkopf, and M. Franz, A Nonparametric Approach to Bottom-Up Visual Saliency, 2007.
  41. J. Tilke, K. Ehinger, F. Durand, and A. Torralba, “Learning to predict where humans look,” in Proceedings of the 12th International Conference on Computer Vision (ICCV '09), pp. 2106–2113, October 2009. View at Publisher · View at Google Scholar · View at Scopus
  42. W. Wang, Y. Wang, Q. Huang, and W. Gao, “Measuring visual saliency by site entropy rate,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '10), pp. 2368–2375, June 2010. View at Publisher · View at Google Scholar · View at Scopus
  43. O. Tuzel, F. Porikli, and P. Meer, “Region covariance: a fast descriptor for detection and classification,” in Computer Vision—ECCV 2006, vol. 3952 of Lecture Notes in Computer Science, pp. 589–600, 2006. View at Publisher · View at Google Scholar · View at Scopus
  44. https://sites.google.com/site/sparsenonlinearsaliencymodel/home/downloads.
  45. A. Hyvärinen and E. Oja, “A fast fixed-point algorithm for independent component analysis,” Neural Computation, vol. 9, no. 7, pp. 1483–1492, 1997. View at Google Scholar · View at Scopus
  46. X. Hou and L. Zhang, “Dynamic visual attention: searching for coding length increments,” in Advances in Neural Information Processing Systems, vol. 21, pp. 681–688, 2008. View at Google Scholar
  47. 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, article 32, pp. 1–20, 2008. View at Publisher · View at Google Scholar · View at Scopus
  48. J. Zhang and S. Sclaroff, “Saliency detection: a boolean map approach,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV '13), 2013.
  49. 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. View at Publisher · View at Google Scholar · View at Scopus
  50. E. Rahtu, J. Kannala, M. Salo, and J. Heikkila, “Segmenting salient objects from images and videos,” in Computer Vision—ECCV 2010, vol. 6315 of Lecture Notes in Computer Science, pp. 366–379, 2010. View at Publisher · View at Google Scholar
  51. M.-M. Cheng, G.-X. Zhang, N. J. Mitra, X. Huang, and S.-M. Hu, “Global contrast based salient region detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '11), pp. 409–416, June 2011. View at Publisher · View at Google Scholar · View at Scopus
  52. T. Veit, J.-P. Tarel, P. Nicolle, and P. Charbonnier, “Evaluation of road marking feature extraction,” in Proceedings of the 11th International IEEE Conference on Intelligent Transportation Systems (ITSC '08), pp. 174–181, December 2008. View at Publisher · View at Google Scholar · View at Scopus
  53. J. P. Jones and L. A. Palmer, “An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex,” Journal of Neurophysiology, vol. 58, no. 6, pp. 1233–1258, 1987. View at Google Scholar · View at Scopus