Research on biologically-inspired computional model for image saliency detection

Hongwai yu Jiguang Gongcheng/Infrared and Laser Engineering

Volumn 42, Issue 3, 2013, Pages 823-828

  Ji, Chao ^a   Liu, Huiying ^a   Shao, Gang ^a   Sun, Jingfeng ^a  

^a NORTHWESTERN POLYTECHNICAL UNIVERSITY   (China)

Author keywords

Biologically inspired; Machine vision; Saliency detection; Sparse representaion

Indexed keywords

BIOLOGICALLY-INSPIRED; BIOLOGICALLY-INSPIRED MODEL; FEATURE COMBINATION; IMAGE CALCULATIONS; REGIONS OF INTEREST; SALIENCY DETECTION; SPARSE REPRESENTAION; SPARSE REPRESENTATION;

COMPUTER VISION; IMAGE SEGMENTATION;

ALGORITHMS;

EID: 84879134091     PISSN: 10072276     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (20)

1. Koene A R, Zhaoping L. Feature-specific interactions in Salience from Combined Feature Contrasts: Evidence for a Bottom-Up Saliency Map in V1 [J]. Journal of Vision, 2007, 7(7): 1-14.
2. Itti L, Koch C, Niebur E. A model of saliency-based visual attention for rapid scene analysis [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20 (11): 1254-1259.
3. Achanta R, Hemami S, Estrada F, et al. Frequency-tuned salient region detection [C]//IEEE Conference on Computer Vision and Pattern Recognition, 2009: 1597-1604.
4. Goferaman S, Zelnik L, Tal A. Context aware saliency detection [C]//IEEE Conference on Computer Vision and Pattern Recognition, 2010: 2376-2383.
5. Hou X, Zhang L. Saliency Detection: A Spectral Residual Approach [C]//IEEE Conference on Computer Vision and Pattern Recognition, 2007: 1-8.
6. Wang W, Wang Y, Huang Q, et al. Measuring visual saliency by site entropy rate [C]//IEEE Conference on Computer Vision and Pattern Recognition, 2010: 135-143.
7. Hou X, Zhang L. Dynamic visual attention: searching for coding length increments[C]//Advances in Neural Information Processing Systems(NIPS), 2008: 35-41.
8. Lin Y, Fang B, Yuanyan T. A computational model for saliency maps by using local entropy [C]//Proceedings of Twenty-Fourth AAAI Conference of Artificial Intelligence, 2010: 967-973.
9. Yang Yawei, Li Junshan, Yang Wei, et al. Multiclass and multiview object detection approach based on sparse biological vision features[J]. Infrared and laser Engineering, 2012, 41(1): 267-272. (in Chinese)
10. Cheng M M, Zhang G X, Mitra N J. Global contrast based salient region detection [C]//IEEE Conference on Computer Vision and Pattern Recognition, 2011: 409-416.
11. F Shahbaz Khan, R Muhammad Anwer, van de weijer, et al. Color attributes for object detection[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2012.
12. Wang Z, Li B. A two-stage approach to saliency detection in images [C]//Proceeding of IEEE International Conference on Acoustics Speech and Signal Processing, 2008: 965-968.
13. He S, Han J, Hu X, et al. A Biologically inspired computational model for image saliency detection [C]//MM'11 Proceedings of the 19th ACM International Conference, NY, USA, 2011: 1465-1468.
14. Bi Fukun, Gao Lining, Long Teng, et al, Remote sening target diction based on visual saliency guidance and classifier fusion [J]. Infrared and laser Engineering, 2011, 40 (10): 2058-2064. (in Chinese)
15. Bruce N, Tsotsos J K. Saliency, attention, and visual search: an information theoretic approach [J]. Journal of Vision, 2009, 9(3): 1-24.
16. Hou X, Zhang L. Saliency detection: a spectral residual approach [C]//IEEE Conference on Computer Vision and Pattern Recognition, 2007: 1-8.
17. Olshausen B. Emergence of simple-cell receptive field properties by learning a sparse code for natural images [J]. Nature, 1996, 38(3): 607-609.
18. Hou X, Harel J, Koch C. Image signature: highlighting sparse salient regions [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(1): 194-201.
19. Seo H, Milanfar P. Nonparametric bottom-up saliency detection by self-resemblance [C]//IEEE Conference of Computer Vision and Pattern Recognition, 2009: 420-426.
20. Zhang L, Tong M H, Marks K T, et al. SUN: a Bayesian framework for saliency using natural statistics[J]. Journal of Vision, 2008, 8(7): 1-10.