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Salient object detection using color spatial distribution and minimum spanning tree weight

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Abstract

Salient object detection is very useful in many computer vision applications such as image segmentation, content-based image editing and object recognition. In this paper, we present a salient object detection algorithm by using color spatial distribution (CSD) and minimum spanning tree weight (MSTW). We first use a segmentation algorithm to decompose an image into superpixel-level elements, then use these elements as nodes to construct a minimum spanning tree (MST), each connected edge weight is the mean color difference between two nodes. CSD of each element can be computed by integrating color, spatial distance and MSTW. Note that if the color of one element is the most widely distributed over the entire image, it should have the biggest CSD value, we regard this element as a background node (BG Node). Then we use the MSTW between other element and BG node to generate a MSTW map. The superpixel-level saliency map can be obtained by combining the CSD map and MSTW map. Finally, we use a guided filter to get the pixel-level saliency map. Experimental results on two databases demonstrate that our proposed method outperforms other previous state-of-the-art approaches.

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References

  1. Achanta R, Hemami S, Estrada F, Susstrunk S (2009) Frequency-tuned salient region detection. In: CVPR, pp 1597–1604

  2. Chen T, Tan P, Ma L, Cheng M, Shamir A, Hu S Poseshop: human image database construction and personalized content synthesis. IEEE Trans Vis Comput Graph 19(5):824–837

  3. Cheng M, Zhang G, Mitra N, Huang X, Hu S (2011) Global contrast based salient region detection. In: CVPR, pp 409–416

  4. Cheng MM, Warrell J, Lin WY, Zheng S, Vineet V, Crook N (2013) Efficient salient region detection with soft image abstraction. In: IEEE ICCV, pp 1529–1536

  5. Erdem E, Erdem A (2013) Visual saliency estimation by nonlinearly integrating features using region covariances. J Vis 13(4):11

    Article  MathSciNet  Google Scholar 

  6. Foncubierta-Rodríguez A, Müller H, Depeursinge A (2013) Region-based volumetric medical image retrieval. In: SPIE medical imaging. international society for optics and photonics

  7. Gao Y, Tang J, Hong R, Yan S, Dai Q, Zhang N, Chua TS (2012) Camera constraint-free view-based 3-d object retrieval. IEEE Trans Image Process 21(4):2269–2281

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  9. Han J, Ngan KN, Li M, Zhang HJ (2006) Unsupervised extraction of visual attention objects in color images. IEEE Trans Circ Syst Video Technol 16(1):141–145

    Article  Google Scholar 

  10. Harel J, Koch C, Perona P (2007) Graph-based visual saliency. In: Neural information processing systems, pp 545–552

  11. He K, Sun J, Tang X (2010) Guided image filtering. In: ECCV, pp 1–14

  12. Hou X, Zhang L (2007) Saliency detection: a spectral residual approach. In: CVPR, pp 1–8

  13. 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(11):1254–1259

    Article  Google Scholar 

  14. Jing H, Han Q, He X, Niu X (2013) Background contrast based salient region detection. Neurocomputing 124:57–62

    Article  Google Scholar 

  15. Kim J, Han D, Tai YW, Kim J (2014) Salient region detection via high-dimensional color transform. In: 2014 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 883–890

  16. Li X, Li Y, Shen C, Dick A, Hengel Avd (2013) Contextual hypergraph modelling for salient object detection. In: ICCV

  17. Liang Z, Wang M, Zhou X, Lin L, Li W (2014) Salient object detection based on regions. Multimed Tools Appl 68(3):517–544

    Article  Google Scholar 

  18. Liu Q, Han T, Sun Y, Chu Z, Shen B (2013) A two step salient objects extraction framework based on image segmentation and saliency detection. Multimed Tools Appl 67(1):231–247

    Article  Google Scholar 

  19. Liu S, He D, Liang X (2012) An improved hybrid model for automatic salient region detection. IEEE Signal Process Lett 19(4):207–210

    Article  Google Scholar 

  20. 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(2):353–367

    Article  Google Scholar 

  21. Liu Y, Li X, Wang L, Niu Y, Liu F (2012) Oscillation analysis for salient object detection. Multimed Tools Appl:1–21

  22. Liu Z, Le Meur O, Luo S, Shen L (2013) Saliency detection using regional histograms. Opt Lett 38(5):700–702

    Article  Google Scholar 

  23. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  24. Margolin R, Tal A, Zelnik-Manor L (2013) What makes a patch distinct? In: 2013 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 1139–1146

  25. Movahedi V, Elder JH (2010) Design and perceptual validation of performance measures for salient object segmentation. In: 2010 IEEE computer society conference on computer vision and pattern recognition workshops (CVPRW). IEEE, pp 49–56

  26. Navalpakkam V, Itti L (2006) An integrated model of top-down and bottom-up attention for optimizing detection speed. In: CVPR, pp 2049–2056

  27. Perazzi F, Krähenbühl P, Pritch Y, Hornung A (2012) Saliency filters: contrast based filtering for salient region detection. In: CVPR, pp 733–740

  28. Rutishauser U, Walther D, Koch C, Perona P (2004) Is bottom-up attention useful for object recognition? In: CVPR, pp 37–44

  29. Shen X, Wu Y (2012) A unified approach to salient object detection via low rank matrix recovery. In: 2012 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 853–860

  30. Thanh Nguyen D, Ogunbona PO, Li W (2013) A novel shape-based non-redundant local binary pattern descriptor for object detection. Pattern Recog 46(5):1485–1500

    Article  Google Scholar 

  31. Tong N, Lu H, Zhang L, Ruan X (2014) Saliency detection with multi-scale superpixels. IEEE Signal Process Lett 21(9):1035–1039

    Article  Google Scholar 

  32. Veksler O, Boykov Y, Mehrani P (2010) Superpixels and supervoxels in an energy optimization framework. In: ECCV, pp 211–224

  33. Wei Y, Wen F, Zhu W, Sun J (2012) Geodesic saliency using background priors. In: ECCV, pp 29–42

  34. Wu H, Wang YS, Feng KC, Wong TT, Lee TY, Heng PA (2010) Resizing by symmetry-summarization. ACM Trans Graph (TOG) 29(6):159

    Article  Google Scholar 

  35. Yang Q (2014) Stereo matching using tree filtering. IEEE Trans Pattern Anal Mach Intell. doi:10.1109/TPAMI.2014.2353642

  36. Yu JG, Tian J (2012) Saliency detection using midlevel visual cues. Optics Lett 37(23):4994–4996

    Article  Google Scholar 

  37. Zhu W, Liang S, Wei Y, Sun J (2014) Saliency optimization from robust background detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2814–2821

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Acknowledgments

This work is funded by the 863 Program of China (2012AA03A301), NSFC (61201179 and 91320201), Ph.D. Pro-grams Foundation of the Ministry of Education of China (20130032110010).

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Correspondence to Zhanjie Song.

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Tang, C., Hou, C., Wang, P. et al. Salient object detection using color spatial distribution and minimum spanning tree weight. Multimed Tools Appl 75, 6963–6978 (2016). https://doi.org/10.1007/s11042-015-2622-5

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  • DOI: https://doi.org/10.1007/s11042-015-2622-5

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