Main subject detection via adaptive feature refinement

Journal of Electronic Imaging

Volumn 20, Issue 1, 2011, Pages

  Vu, Cuong ^a   Chandler, Damon ^a  

^a Oklahoma State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE FEATURES; FEATURE MAP; IMAGE DATABASE; ITERATIVE FASHION; LOW-LEVEL FEATURES; MAIN SUBJECT DETECTION; SPATIAL REGIONS; THREE STAGES;

REFINING;

ALGORITHMS;

EID: 80055086922     PISSN: 10179909     EISSN: 1560229X     Source Type: Journal    
DOI: 10.1117/1.3549884     Document Type: Article

References (43)

1. J. Luo, "Subject content-based intelligent cropping of digital photos," in IEEE International Conf. on Multimedia and Expo, pp. 2218-2221, Beijing, China (2007).
2. S. Banerjee and B. L. Evans, "Unsupervised automation of photographic composition rules in digital still cameras," Proc. SPIE 5301, 364-373 (2004).
3. 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, 771-780, ACM, New York, NY, USA (2006). (Pubitemid 44032166)
4. F. Stentiford, "Attention Based Auto Image Cropping," in ICVS Workshop on Computational Attention & Applications, Bielefeld, Germany (2007).
5. L. Itti, Models of Bottom-Up and Top-Down Visual Attention, Ph.D. thesis, California Institute of Technology, Pasadena, California (2000).
6. L. Wei, N. Sang, Y. Wang, D. Wang, and F. Wang, "Variable resolution image compression based on a model of visual attention," Proc. SPIE 7495, 749501 (2009).
7. S. X. Yu and D. A. Lisin, "Image compression based on visual saliency at individual scales," in International Symposium on Visual Computing (1), Las Vegas, NV, USA, 157-166 (2009).
8. W. Osberger, N. Bergmann, and A. Maeder, "An automatic image quality assessment technique incorporating higher level perceptual factors," Proc. IEEE Int. Conf. on Image Processing, Chicago, IL, USA, Vol. 3, pp. 414-418 (1998).
9. E. C. Larson andD.M. Chandler, "Unveiling relationships between regions of interest and image fidelity metrics," Proc. SPIE 6822, 68222A (2008).
10. U. Engelke, V. X. Nguyen, and H.-J. Zepernick, "Regional attention to structural degradations for perceptual image quality metric design," in IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 869-872 (2008).
11. D. Walther, L. Itti, M. Riesenhuber, T. Poggio, and C. Koch, "Attentional selection for object recognition - a gentle way," in Proc. of 2ndWorkshop on Biologically Motivated Computer Vision (BMCV'02), pp. 472-479, Springer, New York (2002).
12. U. Rutishauser, D. Walther, C. Koch, and P. Perona, "Is bottomup attention useful for object recognition?," IEEE Computer Society Conference on Computer Vision and Pattern Recognition,Washington, DC, USA, Vol. 2, pp. 37-44, 2004.
13. V. Navalpakkam and L Itti, "An integrated model of top-down and bottom-up attention for optimizing detection speed," in CVPR '06: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE Computer Society, Washington, DC, pp. 2049-2056 (2006). (Pubitemid 44931567)
14. L. Itti, C. Koch, and E. Niebur, "A model of saliency-based visual attention for rapid scene analysis," IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1254-1259 (1998). (Pubitemid 128742001)
15. J. Harel, C. Koch, and P. Perona, "Graph-based visual saliency," in Advances in Neural Information Processing Systems 19 pp. 545-552, MIT, Cambridge, MA (2007).
16. O. Le Meur, P. Le Callet, D. Barba, and D. Thoreau, "A coherent computational approach to model bottom-up visual attention," IEEE Trans. Pattern Anal. Mach. Intell. 28(5), 802-817 (2006).
17. S. Frintrop, M. Klodt, and E. Rome, "A real-time visual attention system using integral images," in Proc. of the 5th International Conference on Computer Vision Systems (ICVS) (2007).
18. N. Bruce and J. Tsotsos, "Saliency based on information maximization," Adv. Neural Info. Process. Syst. 18, 155-162 (2006).
19. D. Gao and N. Vasconcelos, "Bottom-up saliency is a discriminant process," in IEEE 11th International Conference on Computer Vision, ICCV, pp. 1-6 (2007).
20. T. Kadir and M. Brady, "Saliency, scale and image description," Int. J. Comput. Vision 45(2), 83-105 (2001). (Pubitemid 33125858)
21. T. Kadir, A. Zisserman, and M. Brady, "An affine invariant salient region detector," in European Conference on Computer Vision, pp. 228-241 (2004).
22. U. Engelke, H. Zepernick, and A. Maeder, "Visual attentionmodeling: region-of-interest versus fixation patterns," in PCS'09: Proceedings of the 27th Conference on Picture Coding Symposium, IEEE Press, Piscataway, NJ, pp. 521-524 (2009).
23. J. Wang, D. M. Chandler, and P. Le Callet, "Quantifying the relationship between visual salience and visual importance," Proc. SPIE 7527, 75270K (2010).
24. Y. Ma and H. Zhang, "Contrast-based image attention analysis by using fuzzy growing," in MULTIMEDIA '03: Proceedings of the Eleventh ACM International Conference on Multimedia, pp. 374-381, New York, NY (2003).
25. X. Hou and L. Zhang, "Saliency detection: A spectral residual approach," in IEEE Conference on Computer Vision and Pattern Recognition (CVPR07), IEEE Computer Society, Minneapolis, Minnesota, USA, pp. 1-8 (2007).
26. Z. Wang and B. Li, "A two-stage approach to saliency detection in images," in IEEE Conference on Acoustics, Speech and Signal Processing, Las Vegas, NV, USA, pp. 965-968 (2008).
27. J. Luo, A. Singhal, S. P. Etz, and R. T. Gray, "A computational approach to determination of main subject regions in photographic images," Image and Vision Computing, 22(3), 227-241 (2004).
28. R. Achanta, F. Estrada, P. Wils, and S. Süsstrunk, "Salient Region Detection and Segmentation," in International Conference on Computer Vision Systems (ICVS '08), pp. 66-75, SpringerBerlin/Heidelberg (2008). (Pubitemid 351775955)
29. R. Achanta, S. S. Hemami, F. J. Estrada, and S. Süsstrunk, "Frequency-tuned salient region detection," in IEEE Conference on Computer Vision and Pattern Recognition, CVPR '09, pp. 1597-1604, Miami, Florida, USA, IEEE (2009).
30. Y. Hu, X. Xie, W. Ma, L. Chia, and D. Rajan, "Salient region detection using weighted feature maps based on the human visual attention model," in Fifth IEEE Pacifc-Rim Conference on Multimedia, Tokyo Waterfront City, pp. 993-1000 (2004). (Pubitemid 39746143)
31. V. Gopalakrishnan, Y. Hu, and D. Rajan, "Salient region detection by modeling distributions of color and orientation," IEEE Trans. Multimedia, 11(5), 892-905 (2009).
32. T. Liu, J. Sun, N. N. Zheng, X. Tang, and H. Y. Shum, "Learning to detect a salient object," in Computer Vision and Pattern Recognition, CVPR '07, IEEE Conference, Minneapolis, Minnesota, USA, June 2007, pp. 1-8.
33. J. D. Lafferty, A. McCallum, and F. Pereira, "Conditional random fields: Probabilistic models for segmenting and labeling sequence data," in ICML '01: Proceedings of the Eighteenth International Conference on Machine Learning, San Francisco, CA, pp. 282-289, MorganKaufmann Publishers Inc, Burlington, Massachusetts,USA(2001).
34. L. E. Baum, T. Petrie, G. Soules, and N. Weiss, "A maximization technique occurring in the statistical analysis of probabilistic functions of markov chains," Ann. Math. Stat. 41(1), 164-171 (1970).
35. L. E. Baum, "An inequality and associated maximization technique in statistical estimation for probabilistic functions of markov processes," Proceedings of the Third Symposium on Inequalities, New York, NY, USA 3, 1-8 (1972).
36. J. Pearl, "Bayesian networks," Handbook of Brain Theory and Neural Network, pp. 149-153, MIT Press, Cambridge, MA, USA (1995).
37. J. B. Macqueen, "Some methods for classification and analysis ofmultivariate observations," in Procedings of the Fifth Berkeley Symposium on Math, Statistics, and Probability, Vol. 1, pp. 281-297, University of California Press, Berkely (1967).
38. D. Comaniciu and P. Meer, "Mean shift: a robust approach toward feature space analysis," IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603-619 (2002). (Pubitemid 34550429)
39. B. Moulden, F. A. A. Kingdom, and L. F. Gatley, "The standard deviation of luminance as a metric for contrast in random-dot images," Perception, 19, 79-101 (1990). (Pubitemid 20234771)
40. C. Vu and D. M. Chandler, "S3: A spectral and spatial sharpness measure," in 2009 First International Conference on Advances in Multimedia, pp. 37-43, Colmar, France (2009).
41. L. G. Roberts, "Machine perception of three-dimensional solids," Optical and Electrooptical Information Processing,MIT Press, Cambridge, MA (1965).
42. J. Freixenet, X. Munoz, D. Raba, J. Marti, and X. Cufi, "Yet another survey on image segmentation: Region and boundary information integration," in ECCV '02: Proceedings of the 7th European Conference on Computer Vision-Part III, pp. 408-422, Springer-Verlag, London (2002).
43. S. Pinneli and D.M. Chandler, "A bayesian approach to predicting the perceived interest of objects," 15th IEEE International Conference on Image Processing, ICIP, pp. 2584-2587, San Diego, CA, USA, 2008.