An Approach to Automatic Detection and Extraction of Regions of Interest in Still Images

  • Dariusz FrejlichowskiEmail author
  • Kamil Grzegorzewicz
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 184)


Localisation and extraction of a salient region in an image constitutes an important task in computer vision. It is a very challenging problem due to its ill-posed nature. Furthermore, it is not always evident even for humans. In this paper we present an approach for automatic detection and extraction of salient regions. The main core of the algorithm is based on Ullman and Koch’s model of the bottom-up attention, however their approach usually predicts only the place of a salient region. Therefore, in order to improve the achieved results a segmentation method based on k-means has been adopted. In addition, a few heuristic limitations are applied to the previous segmentation method in order to prevent it from creating inadequate segments. We show that our approach can render quite precise results in localisation and extraction tasks, taking into account the human point of view.


Salient Region Salient Detection Gaussian Pyramid Primary Saliency Automate Object Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Anouncia, S.M., Joseph, J.G.: An Approach for Automated Object Recognition and Extraction from Images — a Study. Journal of Computing and Information Technology 17(4), 359–370 (2009)Google Scholar
  2. 2.
    Judd, T., Ehinger, K., Durand, F., Torralba, A.: Learning to Predcit Where Humans Look. In: IEEE Int. Conf. on Computer Vission, ICCV 2009 (2009)Google Scholar
  3. 3.
    Ko, B.C., Nam, J.Y.: Object-of-Interest Image Segmentation Based on Human Attention and Semantic Region Clustering. Optical Society of America 23(10), 2462–2470 (2006)CrossRefGoogle Scholar
  4. 4.
    DeCarlo, D., Santella, A.: Stylization and Abstraction of Photographs. ACM Trans. Graph. 21(3), 769–776 (2002)CrossRefGoogle Scholar
  5. 5.
    Jain, A.K., Ratha, N.K., Lakshmanan, S.: Object Detection Using Gabor Filters. Pattern Recognition 30(2), 295–309 (1997)CrossRefGoogle Scholar
  6. 6.
    Koch, C., Ullman, S.: Shifts in Selective Visual-Attention — Towards the Underlying Neural Circuitry. Human Neurobiology 4, 219–277 (1985)Google Scholar
  7. 7.
    Walther, D., Koch, C.: Modeling Attention to Salient Proto-objects. Neural Networks 19(9), 1395–1407 (2006)zbMATHCrossRefGoogle Scholar
  8. 8.
    Zhang, J., Zhuo, L., Shen, L.: Region of Interest Extraction Based on Visual Attention Model and Watershed Segmentation. In: IEEE International Conference on Neural Networks & Signal Processing, pp. 375–378 (2008)Google Scholar
  9. 9.
    Liu, T., Sun, J., Zheng, N.N., Tang, X., Shum, H.Y.: Learning to Detect A Salient Object. In: IEEE Conf. on Computer and Vision Pattern Recognition, pp. 1–8 (2007)Google Scholar
  10. 10.
    Larlus, D., Jurie, F.: Combining Appearance Models and Markov Random Fields for Category Level Object Segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 1–7 (2008)Google Scholar
  11. 11.
    Aldavaret, D., Ramisa, A., Toledo, R., López de Mántaras, R.: Fast and Robust Object Segmentation with the Integral Linear Classifier. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 1046–1053 (2010)Google Scholar
  12. 12.
    Murphy, K., Torralba, A., Eaton, D., Freeman, W.: Object Detection and Localization Using Local and Global Features. In: Ponce, J., Hebert, M., Schmid, C., Zisserman, A. (eds.) Toward Category-Level Object Recognition. LNCS, vol. 4170, pp. 382–400. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  13. 13.
    Marszalek, M., Schimd, C.: Semantic Hierarchies for Visual Object Recognition. In: IEEE Conf. on Computer Vision and Pattern Recogniton, pp. 1–7 (2007)Google Scholar
  14. 14.
    Wang, J., Cohen, M.F.: Image and Video Matting: a Survey. Foundations and Trends in Computer Graphics and Vision 3(2), 97–175 (2007)CrossRefGoogle Scholar
  15. 15.
    Athi Narayanan, S.: Homepage,
  16. 16.
    Oliva, A., Torralba, A.: Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope. Int. J. Comput. Vision 42(3), 145–175 (2001)zbMATHCrossRefGoogle Scholar

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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Faculty of Computer Science and Information TechnologyWest Pomeranian University of Technology, SzczecinSzczecinPoland

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