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Salient object detection method using random graph

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Abstract

In this paper, a bottom-up salient object detection method is proposed by modeling image as a random graph. The proposed method starts with portioning input image into superpixels and extracting color and spatial features for each superpixel. Then, a complete graph is constructed by employing superpixels as nodes. A high edge weight is assigned into a pair of superpixels if they have high similarity. Next, a random walk prior on nodes is assumed to generate the probability distribution on edges. On the other hand, a complete directed graph is created that each edge weight represents the probability for transmitting random walker from current node to next node. By considering a threshold and eliminating edges with higher probability than the threshold, a random graph is created to model input image. The inbound degree vector of a random graph is computed to determine the most salient nodes (regions). Finally, a propagation technique is used to form saliency map. Experimental results on two challenging datasets: MSRA10K and SED2 demonstrate the efficiency of the proposed unsupervised RG method in comparison with the state-of-the-art unsupervised methods.

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References

  1. Achanta R, Estrada F, Wils P, Süsstrunk S (2008) Salient region detection and segmentation, in: Comput. Vis. Syst., Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 66–75

  2. Achanta R, Hemami S, Estrada F, Susstrunk S (2009) Frequency-tuned salient region detection. In: 2009 I.E. Conf. Comput. Vis. Pattern Recognit, IEEE, pp 1597–1604

    Google Scholar 

  3. Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Süsstrunk S (2012) SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans Pattern Anal Mach Intell 34:2274–2281

    Article  Google Scholar 

  4. Alpert S, Galun M, Basri R, Brandt A (2007) Image segmentation by probabilistic bottom-up aggregation and cue integration. In: 2007 I.E. Conf. Comput. Vis. Pattern Recognit, IEEE, pp 1–8

    Google Scholar 

  5. Borji A (2015) What is a salient object ? A dataset and a baseline model for salient object detection. IEEE Trans Pattern Anal Mach Intell 24:742–756

    MathSciNet  Google Scholar 

  6. Borji A, Cheng M-M, Jiang H, Li J (2015) Salient object detection: a benchmark. IEEE Trans Image Process 24:5706–5722

    Article  MathSciNet  Google Scholar 

  7. Buso V, Benois-Pineau J, Domenger J-P (2015) Geometrical cues in visual saliency models for active object recognition in egocentric videos. Multimed. Tools Appl. 74:10077–10095

    Article  Google Scholar 

  8. Chakraborty S, Mitra P (2016) A dense subgraph based algorithm for compact salient image region detection. Comput Vis Image Underst 145:1–14

    Article  Google Scholar 

  9. Cheng M-M, Mitra NJ, Huang X, Torr PHS, Hu S-M (2015) Global contrast based salient region detection. IEEE Trans Pattern Anal Mach Intell 37:569–582

    Article  Google Scholar 

  10. Elazary L, Itti L, Bayesian A (2010) Model for efficient visual search and recognition. Vis Res 50:1338–1352

    Article  Google Scholar 

  11. Felzenszwalb PF, Huttenlocher DP (2004) Efficient graph based image segmentation. Int Conf Comput Vis:167–181

  12. Fu K, Gu IYH, Yun Y, Gong C, Yang J (2014) Graph construction for salient object detection in videos, 2014. In: 22nd Int. Conf. Pattern Recognit, pp 2371–2376

    Google Scholar 

  13. Gilbert EN (1959) Random graphs. Ann Math Stat 30(4):1141–1144

  14. Gopalakrishnan V, Hu Y, Rajan D (2010) Random walks on graphs for salient object detection in images. IEEE Trans Image Process 19:3232–3242

    Article  MathSciNet  MATH  Google Scholar 

  15. Harel J, Koch C, Perona P (2007) Graph-based visual saliency. In: Advances in neural information processing systems. MIT Press, Cambridge, pp 545–552

  16. Huang S, Wang W, Zhang H (2014) Retrieving images using saliency detection and graph matching. In: 2014 I.E. Int. Conf. Image Process, IEEE, pp 3087–3091

    Google Scholar 

  17. Jiang B, Zhang L (2013) H. Lu, C. Yang, M.-H. Yang, saliency detection via absorbing Markov chain. In: Comput. Vis. (ICCV), 2013 I.E. Int. Conf, pp 1665–1672

    Google Scholar 

  18. Kim C, Kim J, Sim J (2014) Multiscale saliency detection using random walk with restart. IEEE Trans. Circuits Syst. Video Technol. 24:198–210

    Article  Google Scholar 

  19. Li Y, Li Y (2016) A fast and efficient saliency detection model in video compressed-domain for human fixations prediction. Multimed. Tools Appl

  20. Li X, Li Y, Shen C, Dick A, Van Den Hengel A (2013) Contextual hypergraph modeling for salient object detection. In: Proc. IEEE Int. Conf. Comput. Vis, pp 3328–3335

    Google Scholar 

  21. Liu T, Sun J, Zheng N, Tang X, Shum H-Y (2007) Learning to detect a salient object, in: 2007 I.E. Conf. Comput. Vis. Pattern Recognit. IEEE:1–8

  22. Liu T, Yuan Z, Sun J, Wang J, Zheng N, Tang X, Shum HY (2011) Learning to detect a salient object. IEEE Trans Pattern Anal Mach Intell 33:353–367

    Article  Google Scholar 

  23. Rasouli A, Tsotsos JK (2015) Integrating three mechanisms of visual attention for active visual search, in: 8th Int. Symp. Atten. Cogn. Syst, Hambg

    Google Scholar 

  24. Tsotsos J, Rothenstein A (2011) Computational models of visual attention. Scholarpedia 6:6201

    Article  Google Scholar 

  25. Wang W, Wang Y, Huang Q, Gao W (2010) Measuring visual saliency by site entropy rate, in: proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit.: pp. 2368–2375

  26. Xie Y, Lu H, Yang M (2012) Bayesian saliency via low and mid level cues. IEEE Trans Image Process 22:1–1

    MathSciNet  MATH  Google Scholar 

  27. Yan C, Zhang Y, Dai F, Li L (2013) Highly parallel framework for HEVC motion estimation on many-core platform, in: 2013 data compression Conf., IEEE: pp. 63–72

  28. Yan C, Zhang Y, Xu J, Dai F, Li L, Dai Q, Wu F (2014) A highly parallel framework for HEVC coding unit partitioning tree decision on many-core processors. IEEE Signal Process Lett 21:573–576

    Article  Google Scholar 

  29. Yan C, Zhang Y, Xu J, Dai F, Zhang J, Dai Q, Wu F (2014) Efficient parallel framework for HEVC motion estimation on many-Core processors. IEEE Trans Circuits Syst Video Technol 24:2077–2089

    Article  Google Scholar 

  30. Yan C, Xie H, Liu S, Yin J, Zhang Y, Dai Q (2017) Effective Uyghur language text detection in complex background images for traffic prompt identification, IEEE trans. Intell Transp Syst 1–10

  31. Yan C, Xie H, Yang D, Yin J, Zhang Y, Dai Q (2017) Supervised hash coding with deep neural network for environment perception of intelligent vehicles, IEEE trans. Intell. Transp. Syst. 1–12

  32. Yang C, Zhang L, Lu H (2013) Graph-regularized saliency detection with convex-hull-based center prior. Signal Process Lett IEEE 20:637–640

    Article  Google Scholar 

  33. Yang C, Zhang L, Lu H, Ruan X, Yang MH (2013) Saliency detection via graph-based manifold ranking, in: proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit.: pp. 3166–3173

  34. Yu J, Member S, Zhao J, Tian J, Tan Y (2014) Maximal entropy random walk for region-based visual saliency. IEEE Trans Cybern 44:1661–1672

    Article  Google Scholar 

  35. Zhai Y, Wang D, Zhang M, Wang J, Guo F (2016) Fault detection of insulator based on saliency and adaptive morphology. Multimed Tools Appl

  36. Zhang L, Yuan X (2015) Salient object detection with higher order potentials and learning affinity. IEEE Signal Process. Lett. 22:1396–1399

    Article  Google Scholar 

  37. Zhang J, Zhang Y, Li L, Dai Q, Dai F, Yan C (2014) Efficient parallel HEVC intra-prediction on many-core processor. Electron Lett 50:805–806

    Article  Google Scholar 

  38. Zhang Y, Wang X, Dai Q, Yan C, Dai F, Li L (2014) Parallel deblocking filter for HEVC on many-core processor. Electron Lett 50:367–368

    Article  Google Scholar 

  39. Zhou D, Huang J, Schölkopf B (2006) Learning with hypergraphs: clustering, classification, and embedding, Adv. Neural Inf. Process, Syst

    Google Scholar 

Download references

Acknowledgements

This work was supported by the Cognitive Science and Technology Council (CSTC) of Iran under the grant number 3232.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran

    Fatemeh Nouri, Kamran Kazemi & Habibollah Danyali

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  1. Fatemeh Nouri
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  2. Kamran Kazemi
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  3. Habibollah Danyali
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Correspondence to Fatemeh Nouri.

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Nouri, F., Kazemi, K. & Danyali, H. Salient object detection method using random graph. Multimed Tools Appl 77, 24681–24699 (2018). https://doi.org/10.1007/s11042-018-5668-3

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  • DOI: https://doi.org/10.1007/s11042-018-5668-3

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