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A novel position prior using fusion of rule of thirds and image center for salient object detection

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Abstract

Salient object detection is one of the challenging problems in the field of computer vision. Most of the models use a center prior to detect salient objects. They give more weightage to the objects which are present near the center of the image and less weightage to the ones near the corners of the image. But there may be images in which object is placed near the image corner. In order to handle such situation, we propose a position prior based on the combined effect of the rule of thirds and the image center. In this paper, we first segment the image into an optimal number of clusters using Davies-Bouldin index. Then the pixels in these clusters are used as samples to build the Gaussian mixture model whose parameters are refined using Expectation-Maximization algorithm. Thereafter the spatial saliency of the clusters is computed based on the proposed position prior and then combined into a saliency map. The performance is evaluated both qualitatively and quantitatively on six publicly available datasets. Experimental results demonstrate that the proposed model outperforms the seventeen existing state-of-the-art methods in terms of F –measure and area under curve on all the six datasets.

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References

  1. Achanta R, Susstrunk S (2010) Saliency detection using maximum symmetric surround, proceedings in international conference on image processing, pp. 2653–2656

  2. Achanta R, Hemamiz S, Estraday F, Susstrunk S (2009) Frequency-tuned Salient Region Detection, Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, pp. 1597–1604

  3. Alexe B, Deselaers T, Ferrari V (2012) Measuring the objectness of image windows. IEEE Trans Pattern Anal Mach Intell 34(11):2189–2202

    Article  Google Scholar 

  4. Amit Y (2002) 2D target detection and recognition, models, algorithms and networks. MIT Press, Cambridge

    Google Scholar 

  5. Arya R, Singh N, Agrawal RK (2015) A novel hybrid approach for salient object detection using local and global saliency in frequency domain. Multimedia Tools Appl. doi:10.1007/s11042-015-2750-y

    Google Scholar 

  6. Borji A, Itti L (2013) State-of-the-art in visual attention modeling. IEEE Trans Pattern Anal Mach Intell 35:185–207

    Article  Google Scholar 

  7. Borji A, Sihite DN, Itti L (2012) Salient object detection: a benchmark. ECCV 414–429

  8. Borji A, Cheng MM, Jiang H, Li J (2014) Salient object detection: A survey. arXiv preprint arXiv:1411.5878

  9. Bruce NDB, Tsotsos JK (2006) Saliency based on information maximization. Advances in Neural Information and Processing Systems 18:155–162

    Google Scholar 

  10. Cheng MM, Warrell J, Lin WY, Zheng S, Vineet V, Crook N (2013) Efficient salient region detection with soft image abstraction. In Proceedings of the IEEE International Conference on Computer Vision 1529–1536

  11. Cheng M-M, Mitra NJ, Huang X, Torr PHS, Hu S-M (2014) Global contrast based salient region detection. IEEE TPAMI (CVPR 2011)

  12. Cheng MM, Zhang Z, Lin WY, Torr P, BING (2014) Binarized normed gradients for objectness estimation at 300fps. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 3286–3293

  13. Davies DL, DW B (1979) A cluster separation measure. IEEE Trans Pattern Anal Mach Intell 1:224–227

    Article  Google Scholar 

  14. Frintrop S, Rome E, Christensen HI (2010) Computational visual attention systems and their cognitive foundation: a survey. ACM Trans Appl Percept 7:1–39

    Article  Google Scholar 

  15. Fu H, Cao X, Tu Z (2013) Cluster-based Co-saliency detection. IEEE Transactions on Image Processing

  16. Gasparini F, Corchs S, Schettini R (2007) Low quality image enhancement using visual attention. Opt Eng 46:40502-(1–3)

    Article  Google Scholar 

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

    Article  Google Scholar 

  18. Gonzalez RC, Woods RE (2002) Digital image processing. Prentice-Hall Inc

  19. Graefe R, Efenberger W (1996) A novel approach for the detection of vehicles on freeways by real time vision. In Intelligent Vehicles 363–368

  20. Halkidi M, Batistakis Y, Vazirgiannis M (2001) On cluster validation techniques. J Intell Inf Syst 17:107–145

    Article  MATH  Google Scholar 

  21. Han J, Zhang D, Hu X, Guo L, Ren J, Wu F (2014) Background prior based salient object detection via deep reconstruction residual. IEEE Trans Circuits Syst Video Technol

  22. Harel J, Koch C, Perona P (2007) Graph based visual saliency, proceedings of the advances in neural information and processing systems, pp. 545–552 Multimed Tools Appl

  23. X. Hou, Zhang L (2007) Saliency detection: a spectral residual approach, proceedings in ieee conference on computer vision and pattern recognition, pp. 1‑8

  24. İmamoğlu N, Lin W, Fang Y (2013) A saliency detection model using low-level features based on wavelet transform. IEEE Trans Multimedia 15:96–105

    Article  Google Scholar 

  25. Itti L (2000) Models of bottom up and top down visual attention. California Institute of Technology, Pasadena

    Google Scholar 

  26. Itti L, Koch C, Niebur E (1998) A model of saliency based visual attention for rapid scene analysis. IEEE Trans Pattern Anal Mach Intell 20:1254–1259

    Article  Google Scholar 

  27. Jiang H, Wang J, Yuan Z, Wu Y, Zheng N, Li S (2013) Salient object detection: a discriminative regional feature integration approach. IEEE CVPR 2083–2090

  28. Karssemeijer N (2006) Detection of stellate distortions in mammograms. IEEE Trans Med Imaging 15:611–619

    Article  Google Scholar 

  29. Li Z, Itti L (2011) Saliency and gist features for target detection in satellite images. IEEE Trans Image Process 20:2017–2029

    Article  MathSciNet  Google Scholar 

  30. Li X, Lu H, Zhang L, Ruan X (2013) M.-H. Yang, Saliency detection via dense and sparse reconstruction. ICCV

  31. Liu T, Yuan Z, Sun-Wang J, Zheng N, Tang X, Shum HY (2007) Learning to detect a salient object, IEEE Conference on Computer Vision and Pattern Recognition 1–8

  32. Liu Z, Zou W, Meur OL (2014) Saliency tree: a novel saliency detection framework. IEEE Trans Image Process 23:1937–1952

    Article  MathSciNet  Google Scholar 

  33. Mai L, Le H, Niu Y, Liu F (2011) Rule of thirds detection from photograph. IEEE Int Symp Multimedia 91–96

  34. Qi W, Cheng MM, Borji A, Lu H, Bai LF (2015) SaliencyRank: two-stage manifold ranking for salient object detection. Comput Vis Media 1(4):309–320

    Article  Google Scholar 

  35. Ren Z, Hu Y, Chia L, Rajan D (2010) Improved saliency detection based on superpixel clustering and saliency propagation. In ACM-MM

  36. Rother C, Bordeaux L, Hamadi Y, Blake A (2006) Autocollage. ACM SIGGRAPH 25:847–852

    Article  Google Scholar 

  37. Santella A, Agrawala M, Decarlo D, Salesin D, Cohen M (2006) Gaze based interaction for semi automatic photo cropping, proceedings of conference human factors in computing systems, pp. 771–780

  38. Shen X, Wu Y (2012) A unified approach to salient object detection via low rank matrix recovery, Proceedings in IEEE Conference on Computer Vision and Pattern Recognition, pp. 853–860

  39. Singh N, Agrawal RK (2013) Combination of Kullback–Leibler divergence and Manhattan distance measures to detect salient objects. SIViP. doi:10.1007/s11760-013-0457-y

    Google Scholar 

  40. Singh N, Arya R, Agrawal RK (2014) A novel approach to combine features for salient object detection using constrained particle swarm optimization. Pattern Recogn 47:1731–1739

    Article  Google Scholar 

  41. Uijlings JR, van de Sande KE, Gevers T, Smeulders AW (2013) Selective search for object recognition. Int J Comput Vis 104(2):154–171

    Article  Google Scholar 

  42. Vikram TN, Tscherepanow M, Wrede B (2012) A saliency map based on sampling an image into random rectangular regions of interest. Pattern Recogn 45:3114–3124

    Article  Google Scholar 

  43. Wei Y, Wen F, Zhu W, Sun J (2012) Geodesic saliency using background priors. In Comput Vision–ECCV 29–42

  44. Zhang L, Tong MH, Marks TK, Shan H, Cottrell GW (2008) SUN: a Bayesian framework for saliency using natural statistics. J Vis 8:1–20

    Google Scholar 

  45. Zhang D, Han J, Li C, Wang J (2015) Co-saliency detection via looking deep and wide. In CVPR

  46. Zhu W, Liang S, Wei Y, Sun J (2014) Saliency optimization from robust background detection. In: IEEE Conference on Computer Vision and Pattern Recognition. pp 2814–2821

  47. Zhu L, Klein DA, Frintrop S, Cao Z, Cremers AB (2014) AMultisize superpixel approach for salient object detection based on multivariate normal distribution estimation. IEEE Trans Image Process 23:5094–5107

    Article  MathSciNet  Google Scholar 

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Acknowledgments

The authors are indebted to the reviewers for their constructive suggestions which significantly helped in improving the quality of this paper.

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Authors and Affiliations

  1. School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, 110067, India

    Navjot Singh, Rinki Arya & R. K. Agrawal

  2. National Institute of Technology, Uttarakhand, Srinagar, Pauri (Garhwal), 246174, India

    Navjot Singh

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  1. Navjot Singh
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  2. Rinki Arya
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  3. R. K. Agrawal
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Correspondence to Navjot Singh.

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Singh, N., Arya, R. & Agrawal, R.K. A novel position prior using fusion of rule of thirds and image center for salient object detection. Multimed Tools Appl 76, 10521–10538 (2017). https://doi.org/10.1007/s11042-016-3676-8

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  • DOI: https://doi.org/10.1007/s11042-016-3676-8

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