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Salient object detection via region contrast and graph regularization

  • Xingming Wu
  • Mengnan Du
  • Weihai ChenEmail author
  • Jianhua Wang
Research Paper

Abstract

Detection of salient objects in an image is now gaining increasing research interest in computer vision community. In this study, a novel region-contrast based saliency detection solution involving three phases is proposed. First, a color-based super-pixels segmentation approach is used to decompose the image into regions. Second, three high-level saliency measures which could effectively characterize the salient regions are evaluated and integrated in an effective manner to produce the initial saliency map. Finally, we construct a pairwise graphical model to encourage that adjacent image regions with similar features take continuous saliency values, thus producing the more perceptually consistent saliency map. We extensively evaluate the proposed method on three public benchmark datasets, and show it can produce promising results when compared to 14 state-of-the-art salient object detection approaches.

Keywords

salient object detection region contrast region compactness global distinctness graph regularization 

基于区域对比度和图正则化的显著目标检测

摘要

创新点

显著目标检测是计算机视觉领域的一个高度活跃的研究方向。在本文中我们提出了一种基于区域对比度和图正则化的显著目标检测算法。首先, 我们将输入图像分割为感知上相同的超像素区域; 然后, 我们提出三种高级的显著性特征, 这些特征可以很好地表征显著目标, 使用这些特征可以取得准确的显著目标检测表现; 最后, 我们提出了一种基于图模型的显著性优化策略, 该模型通过建模空间上下文关系来优化初始显著图, 最终生成了具有高度空间一致性的显著图。

关键词

显著目标检测 区域对比度 区域紧凑度 全局独特性 图正则化 

References

  1. 1.
    Desimone R, Duncan J. Neural mechanisms of selective visual attention. Annu Rev Neurosci, 1995, 18: 193–222CrossRefGoogle Scholar
  2. 2.
    Treisman A M, Gelade G. A feature-integration theory of attention. Cog Psychol, 1980, 12: 97–136CrossRefGoogle Scholar
  3. 3.
    Achanta R, Hemami S, Estrada F, et al. Frequency-tuned salient region detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Miami, 2009. 1597–1604Google Scholar
  4. 4.
    Cheng M, Mitra N J, Huang X, et al. Global contrast based salient region detection. IEEE Trans Patt Anal Mach Intell, 2015, 37: 569–582CrossRefGoogle Scholar
  5. 5.
    Yang C, Zhang L, Lu H, et al. Saliency detection via graph-based manifold ranking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Portland, 2013. 3166–3173Google Scholar
  6. 6.
    Jiang H, Wang J, Yuan Z, et al. Salient object detection: a discriminative regional feature integration approach. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Portland, 2013. 2083–2090Google Scholar
  7. 7.
    Liu Y, Li X Q, Wang L, et al. Interpolation-tuned salient region detection. Sci China Inf Sci, 2014, 57: 012104Google Scholar
  8. 8.
    Donoser M, Urschler M, Hirzer M, et al. Saliency driven total variation segmentation. In: Proceedings of IEEE International Conference on Computer Vision, Kyoto, 2009. 817–824Google Scholar
  9. 9.
    Hiremath P S, Pujari J. Content based image retrieval using color boosted salient points and shape features of an image. Int J Image Process, 2008, 2: 10–17Google Scholar
  10. 10.
    Feng J, Ma L, Bi F K, et al. A coarse-to-fine image registration method based on visual attention model. Sci China Inf Sci, 2014, 57: 122302Google Scholar
  11. 11.
    Marchesotti L, Cifarelli C, Csurka G. A framework for visual saliency detection with applications to image thumbnailing. In: Proceedings of IEEE International Conference on Computer Vision, Kyoto, 2009. 2232–2239Google Scholar
  12. 12.
    Goferman S, Zelnik-Manor L, Tal A. Context-aware saliency detection. IEEE Trans Patt Anal Mach Intell, 2012, 34: 1915–1926CrossRefGoogle Scholar
  13. 13.
    Wei Y, Wen F, Zhu W, et al. Geodesic saliency using background priors. In: Proceedings of European Conference on Computer Vision, Florence, 2012. 29–42Google Scholar
  14. 14.
    Yan Q, Xu L, Shi J, et al. Hierarchical saliency detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Portland, 2013. 1155–1162Google Scholar
  15. 15.
    Itti L, Koch C, Niebur E. A model of saliency-based visual attention for rapid scene analysis. IEEE Trans Patt Anal Mach Intell, 1998, 11: 1254–1259CrossRefGoogle Scholar
  16. 16.
    Alexe B, Deselaers T, Ferrari V. What is an object? In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, 2010. 73–80Google Scholar
  17. 17.
    Achanta R, Estrada F, Wils P, et al. Salient region detection and segmentation. In: Proceedings of 6th International Conference on Computer Vision Systems, Santorini, 2008. 66–75Google Scholar
  18. 18.
    Perazzi F, Krahenbuhl P, Pritch Y, et al. Saliency filters: contrast based filtering for salient region detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Providence, 2012. 733–740Google Scholar
  19. 19.
    Han J, He S, Qian X, et al. An object-oriented visual saliency detection framework based on sparse coding representations. IEEE Trans Circ Syst Video Technol, 2013, 23: 2009–2021CrossRefGoogle Scholar
  20. 20.
    Zhu W, Liang S, Wei Y, et al. Saliency optimization from robust background detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Columbus, 2014. 2814–2821Google Scholar
  21. 21.
    Han J, Zhang D, Hu X, et al. Background prior based salient object detection via deep reconstruction residual. IEEE Trans Circ Syst Video Technol, 2015, 25: 1309–1321CrossRefGoogle Scholar
  22. 22.
    Ma L, Chen L, Zhang X J, et al. A waterborne salient ship detection method on SAR imagery. Sci China Inf Sci, 2015, 58: 089301Google Scholar
  23. 23.
    Shen X, Wu Y. A unified approach to salient object detection via low rank matrix recovery. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Providence, 2012. 853–860Google Scholar
  24. 24.
    Liu R, Cao J, Lin Z, et al. Adaptive partial differential equation learning for visual saliency detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Columbus, 2014. 3866–3873Google Scholar
  25. 25.
    Chang K Y, Liu T L, Chen H T, et al. Fusing generic objectness and visual saliency for salient object detection. In: Proceedings of IEEE International Conference on Computer Vision, Barcelona, 2011. 914–921Google Scholar
  26. 26.
    Li Y, Hou X, Koch C, et al. The secrets of salient object segmentation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Columbus, 2014. 280–287Google Scholar
  27. 27.
    Khuwuthyakorn P, Robles-Kelly A, Zhou J. Object of interest detection by saliency learning. In: Proceedings of European Conference on Computer Vision, Heraklion, 2010. 636–649Google Scholar
  28. 28.
    Liu T, Yuan Z, Sun J, et al. Learning to detect a salient object. IEEE Trans Patt Anal Mach Intell, 2011, 33: 353–367CrossRefGoogle Scholar
  29. 29.
    Kou F, Li Z, Wen C, et al. Perceptual based content adaptive L0 smoothing. In: Proceedings of 14th Pacific-Rim Conference on Multimedia, Nanjing, 2013. 299–307Google Scholar
  30. 30.
    Achanta R, Shaji A, Smith K, et al. Slic superpixels. EPFL-REPORT-149300. 2010Google Scholar
  31. 31.
    Gopalakrishnan V, Hu Y, Rajan D. Salient region detection by modeling distributions of color and orientation. IEEE Trans Multimedia, 2009, 11: 892–905CrossRefGoogle Scholar
  32. 32.
    Cheng M M, Warrell J, Lin W Y, et al. Efficient salient region detection with soft image abstraction. In: Proceedings of IEEE International Conference on Computer Vision, Sydney, 2013. 1529–1536Google Scholar
  33. 33.
    Margolin R, Tal A, Zelnik-Manor L. What makes a patch distinct? In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Portland, 2013. 1139–1146Google Scholar
  34. 34.
    Yang C, Zhang L, Lu H. Graph-regularized saliency detection with convex-hull-based center prior. IEEE Signal Process Lett, 2013, 20: 637–640MathSciNetCrossRefGoogle Scholar
  35. 35.
    Xu L, Li H, Zeng L, et al. Saliency detection using joint spatial-color constraint and multi-scale segmentation. J Vis Commun Image Represent, 2013, 24: 465–476CrossRefGoogle Scholar
  36. 36.
    Lafferty J, McCallum A, Pereira F C N. Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the 18th International Conference on Machine Learning. San Francisco: Morgan Kaufmann Publishers Inc., 2001. 282–289Google Scholar
  37. 37.
    Zhai Y, Shah M. Visual attention detection in video sequences using spatiotemporal cues. In: Proceedings of the 14th Annual ACM International Conference on Multimedia. New York: ACM, 2006. 815–824CrossRefGoogle Scholar
  38. 38.
    Achanta R, Süsstrunk S. Saliency detection using maximum symmetric surround. In: Proceedings of IEEE International Conference on Image Processing, Hong Kong, 2010. 2653–2656Google Scholar
  39. 39.
    Jiang B, Zhang L, Lu H, et al. Saliency detection via absorbing markov chain. In: Proceedings of IEEE International Conference on Computer Vision, Sydney, 2013. 1665–1672Google Scholar
  40. 40.
    Borji A, Cheng M M, Jiang H, et al. Salient object detection: a benchmark. ArXiv e-prints, 2015Google Scholar
  41. 41.
    Alpert S, Galun M, Brandt A, et al. Image segmentation by probabilistic bottom-up aggregation and cue integration. IEEE Trans Patt Anal Mach Intell, 2012, 34: 315–327CrossRefGoogle Scholar
  42. 42.
    Li Z, Zheng J, Zhu Z, et al. Weighted guided image filtering. IEEE Trans Image Process, 2015, 24: 120–129MathSciNetCrossRefGoogle Scholar

Copyright information

© Science China Press and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Xingming Wu
    • 1
  • Mengnan Du
    • 1
  • Weihai Chen
    • 1
    Email author
  • Jianhua Wang
    • 1
  1. 1.School of Automation Science and Electrical EngineeringBeihang UniversityBeijingChina

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