Light field saliency vs. 2D saliency: A comparative study

Neurocomputing

Volumn 166, Issue , 2015, Pages 389-396

  Zhang, Xudong ^a   Wang, Yi ^a   Zhang, Jun ^a   Hu, Liangmei ^a   Wang, Meng ^a  

^a HEFEI UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

2D image; Focusness information; Light filed; Saliency detection

Indexed keywords

TEXTURES;

2D IMAGES; BACKGROUND CLUTTER; COMPARATIVE STUDIES; FOCUSNESS INFORMATION; LIGHT FIELD MODEL; LIGHT FIELDS; SALIENCY DETECTION; SINGLE IMAGES;

CLUTTER (INFORMATION THEORY);

AREA UNDER THE CURVE; ARTICLE; COMPARATIVE STUDY; CONTROLLED STUDY; IMAGE PROCESSING; IMAGING AND DISPLAY; INFORMATION PROCESSING; LIGHT FIELD; LIGHT RELATED PHENOMENA; MACHINE LEARNING; MEASUREMENT ACCURACY; PRIORITY JOURNAL; QUANTITATIVE ANALYSIS; RECEIVER OPERATING CHARACTERISTIC; RELIABILITY; SALIENCY; TWO DIMENSIONAL IMAGING;

EID: 84931578352     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2015.03.042     Document Type: Article

References (47)

1. U. Rutishauser, D. Walther, C. Koch, P. Perona, Is bottom-up attention useful for object recognition? in: CVPR, 2004, pp. 37-44.
2. G. Sharma, F. Jurie, C. Schmid, Discriminative spatial saliency for image classification, in: CVPR, 2012, pp. 3506-3513.
3. P. Wang, J. Wang, G. Zeng, J. Feng, H. Zha, S. Li, Salient object detection for searched web images via global saliency, in: TOAMI, 2012, pp. 1915-1926.
4. Goferman S., Zelnik-Manor L., Tal A. Context-aware saliency detection. TPAMI 2012, 34(10):1915-1926.
5. Itti L., Koch C., Niebur E. A model of saliency-based visual attention for rapid scene analysis. TPAMI 1998, 20(11):1254-1259.
6. X. Shen, Y. Wu, A unified approach to salient object detection via low rank matrix recovery, in: CVPR, 2012, pp. 853-860.
7. Le Meur O., Le Callet P., Barba D., Thoreau D. A coherent computational approach to model bottom-up visual attention. TPAMI 2006, 28(5):802-817.
8. Liu T., Yuan Z., Sun J., Wang J., Zheng N., Tang X., Shum H.-Y. Learning to detect a salient object. TPAMI 2011, 33(2):353-367.
9. Van De Weijer J., Gevers T., Bagdanov A.D. Boosting color saliency in image feature detection. TPAMI 2006, 28(1):150-156.
10. Koch C., Ullman S. Shifts in selective visual attention. towards the underlying neural circuitry. Human Neurobiol. 1985, 4(4):217-229.
11. Itti L., Koch C. Computational modelling of visual attention. Nat. Rev. Neurosci. 2001, 2(3):194-203.
12. Li Z. A saliency map in primary visual cortex. Trends Cognit. Sci. 2002, 6(1):9-16.
13. P. Jiang, H. Ling, J. Yu, J. Peng, Salient region detection by ufo: uniqueness, focusness and objectness, in: ICCV, 2013, pp. 1976-1983.
14. C. Yang, L. Zhang, H. Lu, X. Ruan, M.-H. Yang, Saliency detection via graph-based manifold ranking, in: CVPR, 2013, pp. 3166-3173.
15. Y. Wei, F. Wen, W. Zhu, J. Sun, Geodesic saliency using background priors, in: ECCV, 2012, pp. 29-42.
16. N. Li, J. Ye, Y. Ji, H. Ling, J. Yu, Saliency detection on light field, in: CVPR, 2014, pp. 2806-2813.
17. R. Ng, M. Levoy, M. Brédif, G. Duval, M. Horowitz, P. Hanrahan, Light field photography with a hand-held plenoptic camera, Technical Report CSTR 2005-02, Stanford University, 2005.
18. A. Lumsdaine, T. Georgiev, The focused plenoptic camera, in: ICCP, 2009, pp. 1-8.
19. M. W. Tao, S. Hadap, J. Malik, R. Ramamoorthi, Depth from combining defocus and correspondence using light-field cameras, in: ICCV, 2013, pp. 673-680.
20. C. Chen, H. Lin, Z. Yu, S.B. Kang, J. Yu, Light field stereo matching using bilateral statistics of surface cameras, in: CVPR, 2014, pp. 1518-1525.
21. S. Wanner, B. Goldluecke, Globally consistent depth labeling of 4d light fields, in: CVPR, 2012, pp. 41-48.
22. Kim C., Hornung A., Heinzle S., Matusik W., Gross M. Multi-perspective stereoscopy from light fields. ACM Transactions on Graphics 2011, 30(6):190:1-190:10.
23. Ghasemi A., Vetterli M. Scale-invariant representation of light field images for object recognition and tracking. Electron. Imaging 2014, 902015.
24. Gross R., Matthews I., Baker S. Appearance-based face recognition and light-fields. TPAMI 2004, 26(4):449-465.
25. K. Maeno, H. Nagahara, A. Shimada, R.-i. Taniguchi, Light field distortion feature for transparent object recognition, in: CVPR, 2013, pp. 2786-2793.
26. Ghasemi A., Kamal M.H., Vetterli M. Computationally efficient background subtraction in the light field domain. Electron. Imaging 2014, 90200.
27. B. Heigl, J. Denzler, H. Niemann, Combining computer graphics and computer vision for probabilistic visual robot navigation, in: AeroSense, 2000, pp. 226-235.
28. D.G. Dansereau, O. Pizarro, S.B. Williams, Decoding, calibration and rectification for lenselet-based plenoptic cameras, in: CVPR, 2013, pp. 1027-1034.
29. Adelson E.H., Wang J.Y.A. Single lens stereo with a plenoptic camera. TPAMI 1992, 14(2):99-106.
30. S.J. Gortler, R. Grzeszczuk, R. Szeliski, M.F. Cohen, The lumigraph, in: SIGGRAPH, 1996, pp. 43-54.
31. H. Nagahara, S. Kuthirummal, C. Zhou, S.K. Nayar, Flexible depth of field photography, in: ECCV, 2008, pp. 60-73.
32. A. Agarwala, M. Dontcheva, M. Agrawala, S. Drucker, A. Colburn, B. Curless, D. Salesin, M. Cohen, Interactive digital photomontage, ACM Transactions on Graphics, 23, 2004, pp. 294-302.
33. H.R. Tavakoli, E. Rahtu, J. Heikkilä, Fast and efficient saliency detection using sparse sampling and kernel density estimation, in: Proceedings of the 17th Scandinavian Conference on Image Analysis, 2011, pp. 666-675.
34. Erdem E., Erdem A. Visual saliency estimation by nonlinearly integrating features using region covariances. J. Vis. 2013, 13(4):11.
35. C. Scharfenberger, A. Wong, K. Fergani, J.S. Zelek, D.A. Clausi, Statistical textural distinctiveness for salient region detection in natural images, in: CVPR, 2013, pp. 979-986.
36. F. Perazzi, P. Krahenbuhl, Y. Pritch, A. Hornung, Saliency filters Contrast based filtering for salient region detection, in: CVPR, 2012, pp. 733-740.
37. W. Zhu, S. Liang, Y. Wei, J. Sun, Saliency optimization from robust background detection, in: CVPR, 2014, pp. 2814-2821.
38. Comaniciu D., Meer P. Mean shift. a robust approach toward feature space analysis. TPAMI 2002, 24(5):603-619.
39. Bruce N., Tsotsos J. Attention based on information maximization. J. Vis. 2007, 7(9):950.
40. Z. Bylinskii, T. Judd, F. Durand, A. Oliva, A. Torralba, Mit Saliency Benchmark, . http://saliency.mit.edu/.
41. Kootstra G., de Boer B., Schomaker L.R. Predicting eye fixations on complex visual stimuli using local symmetry. Cognit. Comput. 2011, 3(1):223-240.
42. R. Achanta, S. Hemami, F. Estrada, S. Susstrunk, Frequency-tuned salient region detection, in: CVPR, 2009, pp. 1597-1604.
43. A. Ciptadi, T. Hermans, J.M. Rehg, An in depth view of saliency, in: BMVC, 2013, pp. 9-13.
44. K. Desingh, K.M. Krishna, D. Rajan, C. Jawahar, Depth really matters, improving visual salient region detection with depth, in: BMVC, 2013, pp. 1-11.
45. H. Peng, B. Li, W. Xiong, W. Hu, R. Ji, Rgbd salient object detection: a benchmark and algorithms, in: ECCV, 2014, pp. 92-109.
46. A. Borji, D.N. Sihite, L. Itti, Salient object detection: a benchmark, in: ECCV, 2012, pp. 414-429.
47. M.-M. Cheng, J. Warrell, W.-Y. Lin, S. Zheng, V. Vineet, N. Crook, Efficient salient region detection with soft image abstraction, in: ICCV, 2013, pp. 1529-1536.