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Robot Intelligence Technology and Applications 2012 pp 1023–1034Cite as

Automatic Image Segmentation Using Saliency Detection and Superpixel Graph Cuts

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 208))

Abstract

Image segmentation, which divides an image into foreground and background, is an important task for several applications in vision area such as object detection and classification. In this paper, we introduce a novel algorithm for automatic image segmentation technique which does not require further learning processes to perform segmentation. To achieve this automatic image segmentation, we incorporate saliency map for an image as an initial cue for image segmentation. An enhanced saliency detection method for generating saliency map is proposed. With over-segmented superpixels for an image and the generated saliency map, we perform image segmentation using graph cuts. To adapt graph cut segmentation to superpixel graph and saliency map, we suggest edge costs for superpixel graph based on Gaussian mixture models (GMM). As a result, superpixel graph enhances computational efficiency for our image segmentation technique and saliency map provides helpful cue for foreground region. We evaluate the performance of our algorithm on MSRA database demonstrate experimental results.

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References

  1. Achanta, R., Hemami, S., Estrada, F., Süsstrunk, S.: Frequency-tuned salient region detection. In: Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1597–1604 (2009)

    Google Scholar 

  2. Achanta, R., Süsstrunk, S.: Saliency detection using maximum symmetric surround. In: Proceedings of the 2010 17th IEEE International Conference on Image Processing, pp. 2653–2656 (2010)

    Google Scholar 

  3. Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: From contours to regions: An empirical evaluation. In: Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2294–2301 (2009)

    Google Scholar 

  4. Boykov, Y.Y., Jolly, M.-P.: Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images. In: Proceedings of the 2001 8th IEEE International Conference on Computer Vision, vol. 1, pp. 105–112 (2001)

    Google Scholar 

  5. Brox, T., Bourdev, L., Maji, S., Malik, J.: Object segmentation by alignment of poselet activations to image contours. In: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2225–2232 (2011)

    Google Scholar 

  6. Goferman, S., Zelnik-Manor, L., Tal, A.: Context-aware saliency detection. In: Proceedings of the 2010 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2376–2383 (2010)

    Google Scholar 

  7. Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: Proceedings of the Conference on Advances in Neural Information Processing Systems, vol. 19, pp. 545–552 (2006)

    Google Scholar 

  8. Itti, L., Braun, J., Lee, D.K., Koch, C.: Attentional modulation of human pattern discrimination psychophysics reproduced by a quantitative model. In: Proceedings of the 1998 Conference on Advances in Neural Information Processing Systems, vol. 2, pp. 789–795 (1998)

    Google Scholar 

  9. Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(11), 1254–1259 (1998)

    Article  Google Scholar 

  10. Jung, C., Kim, C.: A unified spectral-domain approach for saliency detection and its application to automatic object segmentation. IEEE Transactions on Image Processing 21(3), 1272–1283 (2012)

    Article  MathSciNet  Google Scholar 

  11. Judd, T., Ehinger, K., Durand, F., Torralba, A.: Learning to predict where humans look. In: Proceedings of the 2009 IEEE 12th International Conference on Computer Vision, pp. 2106–2113 (2009)

    Google Scholar 

  12. Kim, T.H., Lee, K.M., Lee, S.U.: Nonparametric higher-order learning for interactive segmentation. In: Proceedings of the 2010 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3201–3208 (2010)

    Google Scholar 

  13. Klein, D.A., Frintrop, S.: Center-surround divergence of feature statistics for salient object detection. In: Proceedings of the 2011 IEEE International Conference on Computer Vision, pp. 2214–2219 (2011)

    Google Scholar 

  14. Mori, G.: Guiding model search using segmentation. In: Proceedings of the 2005 10th IEEE International Conference on Computer Vision, vol. 2, pp. 1417–1423 (2005)

    Google Scholar 

  15. Pham, V.-Q., Takahashi, K., Naemura, T.: Foreground-background segmentation using iterated distribution matching. In: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2113–2120 (2011)

    Google Scholar 

  16. Ren, X., Malik, J.: Learning a classification model for segmentation. In: Proceedings of the 2003 9th IEEE International Conference on Computer Vision, pp. 10–17 (2003)

    Google Scholar 

  17. Rosenfeld, A., Weinshall, D.: Extracting foreground masks towards object recognition. In: Proceedings of the 2011 IEEE International Conference on Computer Vision, pp. 1371–1378 (2011)

    Google Scholar 

  18. Rother, C., Kolmogorov, V., Blake, A.: GrabCut: interactive foreground extraction using iterated graph cuts. ACM Transactions on Graphics 23(3), 309–314 (2004)

    Article  Google Scholar 

  19. Sinop, A.K., Grady, L.: A seeded image segmentation framework unifying graph cuts and random walker which yields a new algorithm. In: Proceedings of the 2007 IEEE 11th International Conference on Computer Vision, pp. 1–8 (2007)

    Google Scholar 

  20. Zhai, Y., Shah, M.: Visual attention detection in video sequences using spatiotemporal cues. In: Proceedings of the 14th Annual ACM International Conference on Multimedia, pp. 815–824 (2006)

    Google Scholar 

  21. Achanta, R., Estrada, F., Wils, P., Süsstrunk, S.: Salient region detection and segmentation. In: Proceedings of the 6th International Conference on Computer Vision Systems, pp. 66–75 (2008)

    Google Scholar 

  22. Cheng, M.-M., Zhang, G.-X., Mitra, N.J., Huang, X., Hu, S.-M.: Global contrast based salient region detection. In: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition, pp. 409–416 (2011)

    Google Scholar 

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Authors and Affiliations

  1. Dept. of Electrical Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, 305-701, South Korea

    Sandeul Kang, Hansang Lee, Jiwhan Kim & Junmo Kim

Authors
  1. Sandeul Kang
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  2. Hansang Lee
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  3. Jiwhan Kim
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  4. Junmo Kim
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Editor information

Editors and Affiliations

  1. , Electrical Engineering and Computer Sci., KAIST, 291 Daehak-ro, Daejeon, 305-701, Korea, Republic of (South Korea)

    Jong-Hwan Kim

  2. Dept. Computer &, Information Technology (ICT), Purdue University, N. Grant St. 401, West Lafayette, 47907-1421, Indiana, USA

    Eric T. Matson

  3. , Dept. of Civil and Environmental Engg., KAIST, Daehak-ro 291, Daejeon, 305-701, Korea, Republic of (South Korea)

    Hyun Myung

  4. , Faculty of Engineering, The University of Auckland, Private Bag, Auckland, 1142, New Zealand

    Peter Xu

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Kang, S., Lee, H., Kim, J., Kim, J. (2013). Automatic Image Segmentation Using Saliency Detection and Superpixel Graph Cuts. In: Kim, JH., Matson, E., Myung, H., Xu, P. (eds) Robot Intelligence Technology and Applications 2012. Advances in Intelligent Systems and Computing, vol 208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37374-9_99

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  • DOI: https://doi.org/10.1007/978-3-642-37374-9_99

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37373-2

  • Online ISBN: 978-3-642-37374-9

  • eBook Packages: EngineeringEngineering (R0)

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