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Saliency detection by hierarchically integrating compactness, contrast and boundary connectivity

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Abstract

Saliency detection is one of the most challenging problems in computer vision and has extensive applications in many fields. In this work, instead of simply defining the compactness and contrast, we design novel versions of these two cues based on manifold ranking, and then propose a saliency detection model by integrating the newly modified compactness and contrast with boundary connectivity. Since various scales salient detections highlight different parts of the objects, to further improve the performance, we perform the model hierarchically on four different scales and then fuse the results to obtain the final saliency map. Experiments on four benchmark datasets demonstrate the effectiveness of the proposed method. The method can further improve the accuracy of saliency detection than other 15 state-of-the-art methods on MSRA10k, ASD, DUT-OMRON and ECSSD.

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References

  1. Achanta R, Estrada F, Wils P et al (2008) Salient region detection and segmentation. In: International conference on computer vision systems, Santorini, pp 66–75

  2. Achanta R, Hemami S, Estrada F et al (2009) Frequency-tuned salient region detection. In: IEEE conference on computer vision and pattern recognition, Miami, pp 1597–1604

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

    Article  Google Scholar 

  4. Achanta R, Süsstrunk S (2010) Saliency detection using maximum symmetric surround. In: International conference on image processing, Hong Kong, pp 2653–2656

  5. Chang KY, Liu TL, Chen HT, Lai SH (2011) Fusing generic objectness and visual saliency for salient object detection. In: IEEE International conference on computer vision, Barcelona, pp 914–921

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

    Article  Google Scholar 

  7. Duan L, Wu C, Miao J, Qing L, Fu Y (2011) Visual saliency detection by spatially weighted dissimilarity. In: IEEE conference on computer vision and pattern recognition, Colorado, pp 473–480

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

    Article  Google Scholar 

  9. 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 

  10. Jian M, Dong J, Ma J (2011) Image retrieval using wavelet-based salient regions. Imaging Sci J 59:219–231

    Article  Google Scholar 

  11. Jian M, Lam KM, Dong J (2014) Facial-feature detection and localization based on a hierarchical scheme. Inf Sci 262:1–14

    Article  Google Scholar 

  12. Jian M, Lam KM, Dong J, Shen L (2015) Visual-patch-attention-aware Saliency Detection. IEEE Trans Cybern 45:1575–1586

    Article  Google Scholar 

  13. Jiang H, Wang J, Yuan Z, Liu T, Zheng N (2011) Automatic salient object segmentation based on context and shape prior. In: British machine vision conference, Dundee

  14. Jiang H, Wang J, Yuan Z, Wu Y, Zheng N, Li S (2013) Salient object detection: A discriminative regional feature integration approach. In: IEEE conference on computer vision and pattern recognition, Portland, pp 2083–2090

  15. Jiang P, Ling H, Yu J, Peng J (2013) Salient region detection by UFO: Uniqueness, focusness and objectness. In: IEEE International conference on computer vision, Sydney, pp 1976–1983

  16. Judd T, Ehinger K, Ehinger F, Durand F, Torralba A (2009) Learning to predict where humans look. In: IEEE International conference on computer vision, Kyoto, pp 2106–2113

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

  18. Li X, Lu H, Zhang L, Ruan X, Yang MH (2013) Saliency detection via dense and sparse reconstruction. In: IEEE International conference on computer vision, Sydney, pp 2976–2983

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

    Article  Google Scholar 

  20. Lu H, Li X, Zhang L, Ruan X, Yang MH (2016) Dense and sparse reconstruction error based saliency descriptor. IEEE Trans Image Process 25:1592–1603

    Article  MathSciNet  Google Scholar 

  21. Lu Y, Zhang W, Lu H, Xue X (2011) Salient object detection using concavity context. In: IEEE International conference on computer vision, Barcelona, pp 233–240

  22. Perazzi F, Krähenbühl P, Pritch Y, Hornung A (2012) Saliency filters: Contrast based filtering for salient region detection. In: IEEE Conference on computer vision and pattern recognition, Providence, pp 733–740

  23. Pradeep A, Subash N (2015) Saliency tree: saliency detection method integrating diffusion-based compactness and local contrast. Int J Innov Res Comput Commun Eng 10:9778–9784

    Google Scholar 

  24. Rahtu E, Kannala J, Salo M, Heikkilä J (2010) Segmenting salient objects from images and videos. In: European conference on computer vision, Crete, pp 366–379

  25. Tong N, Lu H, Ruan X, Yang MH (2015) Salient object detection via bootstrap learning. In: IEEE Conference on computer vision and pattern recognition, Boston, pp 1884–1892

  26. Tong N, Lu H, Zhang Y, Ruan X (2015) Salient object detection via global and local cues. Pattern Recogn 48:3258–3267

    Article  Google Scholar 

  27. Wang J, Lu H, Li X, Tong N, Liu W (2015) Saliency detection via background and foreground seed selection. Neurocomputing 152:359–368

    Article  Google Scholar 

  28. Wang L, Xue J, Zheng N, Hua G (2011) Automatic salient object extraction with contextual cue. In: IEEE International conference on computer vision, Barcelona, pp 105–112

  29. Wei Y, Wen F, Zhu W, Sun J (2012) Geodesic saliency using background priors. European conference on computer vision, Firenze, pp 29–42

  30. Yan Q, Xu L, Shi J, Jia J (2013) Hierarchical saliency detection. In: IEEE Conference on computer vision and pattern recognition, Portland, pp 1155–1162

  31. Yang C, Zhang L, Lu H, Ruan X, Yang MH (2013) Saliency detection via graph-based manifold ranking. In: IEEE Conference on computer vision and pattern recognition, Portland, pp 3166–3173

  32. Yang J, Yang MH (2012) Top-down visual saliency via joint crf and dictionary learning. In: IEEE Conference on computer vision and pattern recognition, Providence, pp 2296–2303

  33. Zhang J, Sclaroff S, Lin Z, Shen X, Price B, Mech R (2015) Minimum barrier salient object detection at 80 FPS. In: IEEE International conference on computer vision, Santiage, pp 1404–1412

  34. Zhou D, Weston J, Gretton A, Bousquet O, Schölkopf B (2004) Ranking on data manifolds. In: Advances in neural information processing systems 16: proceedings of the 2003 conference. The MIT Press, Cambridge, pp 169–176

  35. Zhou L, Yang Z, Yuan Q, Zhou Z, Hu D (2015) Salient region detection via integrating diffusion-based compactness and local contrast. IEEE Trans Image Process 24:3308–3320

    Article  MathSciNet  Google Scholar 

  36. Zhou X, Liu Z, Sun G, Wang X (2016) Improving saliency detection via multiple kernel boosting and adaptive fusion. IEEE Signal Process Lett 23:517–521

    Article  Google Scholar 

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

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Acknowledgements

This work is supported by the Natural Science Basic Research Plan in Shaanxi Province of China(No.2015JM6296).

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Authors and Affiliations

  1. School of Natural and Applied Sciences, Northwestern Polytechnical University, Xi’an, 710072, China

    Yanzhao Wang, Guohua Peng & Min Zhou

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  1. Yanzhao Wang
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  2. Guohua Peng
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  3. Min Zhou
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Correspondence to Yanzhao Wang.

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Wang, Y., Peng, G. & Zhou, M. Saliency detection by hierarchically integrating compactness, contrast and boundary connectivity. Multimed Tools Appl 77, 11883–11901 (2018). https://doi.org/10.1007/s11042-017-4839-y

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  • DOI: https://doi.org/10.1007/s11042-017-4839-y

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