Advertisement

Multimedia Tools and Applications

, Volume 67, Issue 1, pp 231–247 | Cite as

A two step salient objects extraction framework based on image segmentation and saliency detection

  • Qiang Liu
  • Tao HanEmail author
  • Yantao Sun
  • Zhong Chu
  • Bingwen Shen
Article

Abstract

Salient objects extraction from a still image is a very hot topic, as it owns a lot of useful applications (e.g., image compression, content-based image retrieval, digital watermarking). In this paper, targeted to improve the performance of the extraction approach, we propose a two step salient objects extraction framework based on image segmentation and saliency detection (TIS). Specially, during the first step, the image is segmented into several regions using image segmentation algorithm and the saliency map for the whole image is detected with saliency detection algorithm. In the second step, for each region, some features are extracted for the SVM algorithm to classify the region as a background region or a salient region twice. Experimental results show that our proposed framework can extract the salient objects more precisely and can achieve a good extraction results, compared with previous salient objects extraction methods.

Keywords

Salient objects extraction Image segmentation Saliency detection Framework SVM 

Notes

Acknowledgements

This work was supported by “the Fundamental Research Funds for the Central Universities” (2012JBM032). Tao Han’s research was supported by Hubei Provincial Science and Technology Department (Grant No.: 2011BFA004).

References

  1. 1.
    Achanta R, Hemami S, Estrada F, Susstrunk S (2009) Frequency-tuned salient region detection. In: Proc. of IEEE computer vision and pattern recognition (CVPR), pp 1597–1604Google Scholar
  2. 2.
    Daubechies I (1990) The wavelet transform, time-frequency localization and signal analysis. IEEE Trans Info Theory 6(5):961–1005MathSciNetCrossRefGoogle Scholar
  3. 3.
    Felzenszwalb P, Huttenlocher D (2004) Efficient graph-based image segmentation. Int J Comput Visn (IJCV) 59(2):167–181CrossRefGoogle Scholar
  4. 4.
    Goferman S, Manor LZ, Tal A (2010) Context-aware saliency detection. In: Proc. of IEEE computer vision and pattern recognition (CVPR), pp 2376–2383Google Scholar
  5. 5.
    Han Z, Li Z, Zhi ZY (2009) Salient object extraction based on region saliency ratio. In: Proc. of the eigth IEEE/ACIS international conference on computer and information scienceGoogle Scholar
  6. 6.
    Harel J, Koch C, Perona P (2007) Graph-based visual saliency. In: Proc. of advances in neural information processing systems (NIPS), vol 19, pp 545–552Google Scholar
  7. 7.
    Hou X, Zhang L (2007) Saliency detection: a spectral residual approach. In: Proc. of IEEE computer vision and pattern recognition (CVPR), pp 1–8Google Scholar
  8. 8.
    Itti L, Koch C, Niebur E (1998) A model of saliency-based visual attention for rapid scene analysis. IEEE Trans Pattern Anal Mach Intell 2(11):1254–1259CrossRefGoogle Scholar
  9. 9.
    Kim S, Park S, Kim M (2003) Central object extraction for object-based image retrieval. In: Proc. of the international conference on image and video retrieval, pp 3949Google Scholar
  10. 10.
    Kwak S, Ko B, Byun H (2004) Automatic salient-object extraction using the contrast map and salient points. In: Proc. of PCM (2), pp 138–14Google Scholar
  11. 11.
    Levinshtein A, Stere A (2009) TurboPixels: fast superpixels using geometric flows. IEEE Trans Pattern Anal Mach Intell 31(12):2290–2297CrossRefGoogle Scholar
  12. 12.
    Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110CrossRefGoogle Scholar
  13. 13.
    Ma YF, Zhang HJ (2003) Contrast-based image attention analysis by using fuzzy growing. In: Proc. of the eleventh ACM international conference on multimedia table of contents, pp 374–381Google Scholar
  14. 14.
    Muller KR, Mika S et al (2001) An Introduction to kernel-based learning algorithm. IEEE Trans Neural Net 12(2):181–201CrossRefGoogle Scholar
  15. 15.
    Osberger W, Naeder AJ (1998) Automatic identification of perceptually important regions in an image. In: Proc. of the IEEE international conference on pattern recognition, pp 701–704Google Scholar
  16. 16.
    Park KT, Moon YS (2007) Automatic extraction of salient objects using feature maps. In: Proc. of the IEEE international conference on acoustics, speech and signal processing (ICASSP)Google Scholar
  17. 17.
    Pawes MA, Tenht WR (1999) Partitioning 3D surface meshes using watershed segmentation. IEEE Trans Vis Comput Graph 5(4):308–321CrossRefGoogle Scholar
  18. 18.
    Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22(8):888–905CrossRefGoogle Scholar
  19. 19.
    Suykens JAK, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3):293–300MathSciNetCrossRefGoogle Scholar
  20. 20.
    Tang C et al (1993) Texture analysis and classification with tree-structured wavelet transform. IEEE Trans Image Process 2(4):429–441CrossRefGoogle Scholar
  21. 21.
    Wang W, Song Y, Zhang A (2002) Semantics retrieval by region saliency. In: Proc. of the international conference on image and video retrieval, pp 2937Google Scholar
  22. 22.
    Zhang J, Zhuo L, Shen LS (2008) Regions of interest extraction based on visual attention model and watershed segmentation. IEEE International Conference on Neural Networks & Signal Processing, pp 375–378, Zhenjiang, ChinaGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Qiang Liu
    • 1
  • Tao Han
    • 2
    Email author
  • Yantao Sun
    • 1
  • Zhong Chu
    • 1
  • Bingwen Shen
    • 1
  1. 1.School of Computer and Information TechnologyBeijing Jiaotong UniversityBeijingChina
  2. 2.Department of Electronics and Information EngineeringHuazhong University of Science and TechnologyHuazhongChina

Personalised recommendations