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Saliency detection based on superpixels clustering and stereo disparity

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Abstract

Reliable saliency detection can be used to quickly and effectively locate objects in images. In this paper, a novel algorithm for saliency detection based on superpixels clustering and stereo disparity (SDC) is proposed. Firstly, we use an improved superpixels clustering method to decompose the given image. Then, the disparity of each superpixel is computed by a modified stereo correspondence algorithm. Finally, a new measure which combines stereo disparity with color contrast and spatial coherence is defined to evaluate the saliency of each superpixel. From the experiments we can see that regions with high disparity can get higher saliency value, and the saliency maps have the same resolution with the source images, objects in the map have clear boundaries. Due to the use of superpixel and stereo disparity information, the proposed method is computationally efficient and outperforms some state-of-the-art colorbased saliency detection methods.

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References

  1. R Achanta, F Estrada, P Wils, et al. Salient region detection and segmentation, In: Computer Vision Systems, Springer Berlin Heidelberg, 2008, 66–75.

    Chapter  Google Scholar 

  2. R Achanta, S Hemami, F Estrada, et al. Frequency-tuned salient region detection, In: IEEE Comput Vis Pattern Recognition (CVPR), 2009, 1597–1604.

    Google Scholar 

  3. R Achanta, A Shaji, K Smith, et al. SLIC superpixels compared to state-of-the-art superpixel methods, IEEE Trans Pattern Anal Mach Intell, 2012, 34(11): 2274–2282.

    Article  Google Scholar 

  4. S Avidan, A Shamir. Seam carving for content-aware image resizing, ACM Trans Graph, 2007, 26(3), Article No 10.

    Article  Google Scholar 

  5. M Cheng, N J Mitra, X Huang, et al. Global contrast based salient region detection, IEEE Trans Pattern Anal Mach Intell, 2015, 37(3): 569–582.

    Article  Google Scholar 

  6. S P Du, S M Hu, R R Martin. Changing perspective in stereoscopic images, IEEE Trans Vis Comput Graph, 2013, 19(8): 1288–1297.

    Article  Google Scholar 

  7. L Duan, C Wu, J Miao, et al. Visual saliency detection by spatially weighted dissimilarity, In: IEEE Comput Vis Pattern Recognition (CVPR), 2011, 473–480.

    Google Scholar 

  8. S Goferman, L Zelnik-Manor, A Tal. Context-aware saliency detection, IEEE Trans Pattern Anal Mach Intell, 2012, 34(10): 1915–1926.

    Article  Google Scholar 

  9. J Han, K N Ngan, M Li, et al. Unsupervised extraction of visual attention objects in color images, IEEE Trans Circuits Systems Video Technol, 2006, 16(1): 141–145.

    Article  Google Scholar 

  10. X Hou, L Zhang. Saliency detection: A spectral residual approach, CIn: IEEE Comput Vis Pattern Recognition (CVPR), 2007, 1–8.

    Google Scholar 

  11. L Itti, P F Baldi. Bayesian surprise attracts human attention, Adv Neural Inf Process Syst, 2005, 547–554.

    Google Scholar 

  12. L Itti, C Koch, E Niebur. A model of saliency-based visual attention for rapid scene analysis, IEEE Trans Pattern Anal Mach Intell, 1998 (11): 1254–1259.

    Article  Google Scholar 

  13. C Koch, S Ullman. Shifts in selective visual attention: towards the underlying neural circuitry, In: Matters of Intelligence, Springer Netherlands, 1987, 115–141.

    Chapter  Google Scholar 

  14. K Koffka. Principles of Gestalt Psychology, Routledge, 2013.

    Google Scholar 

  15. A Levinshtein, S Dickinson, C Sminchisescu. Multiscale symmetric part detection and grouping, In: Proc Internat Conf Comput Vis, 2009, 2162–2169.

    Google Scholar 

  16. T Liu, Z Yuan, J Sun, et al. Learning to detect a salient object, IEEE Trans Pattern Anal Mach Intell, 2011, 33(2): 353–367.

    Article  Google Scholar 

  17. Y F Ma, H J Zhang. Contrast-based image attention analysis by using fuzzy growing, Proc eleventh ACM Internat Conf Multimedia, 2003, 374–381.

    Chapter  Google Scholar 

  18. J Malik. Normalized cuts and image segmentation, IEEE Trans Pattern Anal Mach Intell, 2000, 22(8): 888–905.

    Article  Google Scholar 

  19. S Mattoccia. Stereo vision: algorithms and applications, Technical report, University Of Bologna, 2011.

    Google Scholar 

  20. T J Mu, J J Sun, R R Martin, S M Hu. A response time model for abrupt changes in binocular disparity, Vis Comput, 2014: 1–13.

    Google Scholar 

  21. T J Mu, J H Wang, S P Du, S M Hu. Stereoscopic image completion and depth recovery, Vis Comput, 2014, 30(6-8): 833–843.

    Article  Google Scholar 

  22. D Parkhurst, K Law, E Niebur. Modeling the role of salience in the allocation of overt visual attention, Vis Res, 2002, 42(1): 107–123.

    Article  Google Scholar 

  23. A Radhakrishna, S Appu, S Kevin, L Aurelien. SLI Superpixels, EPFL Report 149300, 2010.

    Google Scholar 

  24. A Radhakrishna, S Appu, S Kevin, L Aurelien, F Pascal, S Sabine. SLIC Superpixels Compared to State-of-the-art Superpixel Methods, IEEE Trans Pattern Anal Mach Intell, 2012, 6(1).

    Google Scholar 

  25. P Reinagel, A M Zador. Natural scene statistics at the centre of gaze, Network, 1999, 10(4): 341–350.

    Article  MATH  Google Scholar 

  26. U Rutishauser, D Walther, C Koch, et al. Is bottom-up attention useful for object recognition? In: IEEE Comput Vis Pattern Recognition (CVPR), 2004, 2: II-37–44.

    Google Scholar 

  27. D Scharstein, R Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms, Internat J Comput Vis, 2002, 47(1-3): 7–42.

    Article  MATH  Google Scholar 

  28. R Swaminathan, S K Nayar. Nonmetric calibration of wide-angle lenses and polycameras, IEEE Trans Pattern Anal Mach Intell, 2000, 22(10): 1172–1178.

    Article  Google Scholar 

  29. R Tong, Y Zhang, K L Cheng. Stereopasting: interactive composition in stereoscopic images, IEEE Trans Vis Comput Graph, 2013, 19(8): 1375–1385.

    Article  Google Scholar 

  30. R Y Tsai. An efficient and accurate camera calibration technique for 3D machine vision, Proc IEEE Conf Comput Vis Pattern Recognition, 1986.

    Google Scholar 

  31. A Vedaldi, S Soatto. Quick shift and kernel methods for mode seeking, In: Computer VisionCECCV 2008, 2008, 705–718.

    Chapter  Google Scholar 

  32. M Wang, J Konrad, P Ishwar, et al. Image saliency: From intrinsic to extrinsic context, In: IEEE Comput Vis Pattern Recognition (CVPR), 2011, 417–424.

    Google Scholar 

  33. J Wang, C Zhang, Y Zhou, et al. Global contrast of superpixels based salient region detection, In: Computational Visual Media, Springer Berlin Heidelberg, 2012, 130–137.

    Chapter  Google Scholar 

  34. K J Yoon, I S Kweon. Adaptive support-weight approach for correspondence search, IEEE Trans Pattern Anal Mach Intell, 2006, 28(4): 650–656.

    Article  Google Scholar 

  35. Y Zhai, M Shah. Visual attention detection in video sequences using spatiotemporal cues, Proc 14th ACM Internat Conf Multimedia, 2006: 815–824.

    Chapter  Google Scholar 

  36. W J Zhu, S Liang, Y C Wei, J Sun. Saliency Optimization from Robust Background Detection, IEEE Conf Comput Vis Pattern Recognition, 2014, 1049–1050. 2814-2821.

    Google Scholar 

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Authors and Affiliations

  1. School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, 250014, China

    Shan-shan Gao, Jing Chi, Li Li & Cai-ming Zhang

  2. School of Computer Science and Technology, Shandong University, Jinan, 250100, China

    Ji-biao Zou & Cai-ming Zhang

  3. Shandong Provincial Key Laboratory of Digital Media Technology, Jinan, 250014, China

    Shan-shan Gao, Jing Chi & Li Li

Authors
  1. Shan-shan Gao
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  2. Jing Chi
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  3. Li Li
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  4. Ji-biao Zou
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  5. Cai-ming Zhang
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Additional information

This work was supported by NSFC Joint Fund with Guangdong under Key Project (U1201258), National Natural Science foundation of China (61402261, 61303088, 61572286), the scientific research foundation of Shandong Province of Outstanding Young Scientist Award (BS2013DX048), Shandong Ji’nan Science and Technology Development Project (201202015).

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Gao, Ss., Chi, J., Li, L. et al. Saliency detection based on superpixels clustering and stereo disparity. Appl. Math. J. Chin. Univ. 31, 68–80 (2016). https://doi.org/10.1007/s11766-016-3365-4

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  • DOI: https://doi.org/10.1007/s11766-016-3365-4

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