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Spatial Hierarchy of Textons Distributions for Scene Classification

  • Sebatiano Battiato
  • Giovanni Maria Farinella
  • Giovanni Gallo
  • Daniele Ravì
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5371)

Abstract

This paper proposes a method to recognize scene categories using bags of visual words obtained hierarchically partitioning into subregion the input images. Specifically, for each subregion the Textons distribution and the extension of the corresponding subregion are taken into account. The bags of visual words computed on the subregions are weighted and used to represent the whole scene. The classification of scenes is carried out by a Support Vector Machine. A k-nearest neighbor algorithm and a similarity measure based on Bhattacharyya coefficient are used to retrieve from the scene database those that contain similar visual content to a given a scene used as query. Experimental tests using fifteen different scene categories show that the proposed approach achieves good performances with respect to the state of the art methods.

Keywords

Scene Classification Textons Spatial Distributions 

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References

  1. 1.
    Torralba, A.: Contextual priming for object detection. International Journal of Computer Vision 53(2), 169–191 (2003)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Vogel, J., Schiele, B.: Semantic modeling of natural scenes for content-based image retrieval. International Journal of Computer Vision 72(2), 133–157 (2007)CrossRefGoogle Scholar
  3. 3.
    Battiato, S., Farinella, G.M., Giuffrida, G., Sismeiro, C., Tribulato, G.: Using visual and text features for direct marketing on multimedia messaging services domain. Multimedia Tools and Applications Journal(in press, 2008)Google Scholar
  4. 4.
    Bosch, A., Zisserman, A., Munoz, X.: Scene classification via pLSA. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 517–530. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  5. 5.
    Fei-Fei, L., Perona, P.: A hierarchical bayesian model for learning natural scene categories. In: IEEE Computer Science Society International Conference of Computer Vision and Pattern Recognition (CVPR), San Diego, CA, USA (June 2005)Google Scholar
  6. 6.
    Renninger, L.W., Malik, J.: When is scene recognition just texture recognition? Vision Research 44, 2301–2311 (2004)CrossRefGoogle Scholar
  7. 7.
    Ladret, P., Guérin-Dugué, A.: Categorisation and retrieval of scene photographs from jpeg compressed database. Pattern Analysis & Application 4, 185–199 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. International Journal of Computer Vision 42, 145–175 (2001)CrossRefzbMATHGoogle Scholar
  9. 9.
    Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. II, pp. 2169–2178 (2006)Google Scholar
  10. 10.
    Matthew, R.B., Jiebo, L.: Beyond pixels: Exploiting camera metadata for photo classification. Pattern Recognition 38(6), 935–946 (2005)CrossRefGoogle Scholar
  11. 11.
    Sivic, J., Zisserman, A.: Video Google: A text retrieval approach to object matching in videos. In: Proceedings of the International Conference on Computer Vision, October 2003, vol. 2, pp. 1470–1477 (2003)Google Scholar
  12. 12.
    Julesz, B.: Textons, the elements of texture perception, and their interactions. Nature 290, 91–97 (1981)CrossRefGoogle Scholar
  13. 13.
    Varma, M., Zisserman, A.: A statistical approach to texture classification from single images. International Journal of Computer Vision 62(1–2), 61–81 (2005)CrossRefGoogle Scholar
  14. 14.
    Shawe-Taylor, J., Cristianini, N.: Support Vector Machines and other kernel-based learning methods. Cambridge University Press, Cambridge (2000)zbMATHGoogle Scholar
  15. 15.
    Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 564–575 (2003)CrossRefGoogle Scholar
  16. 16.
    Dance, C., Willamowski, J., Fan, L., Bray, C., Csurka, G.: Visual categorization with bags of keypoints. In: ECCV International Workshop on Statistical Learning in Computer Vision (2004)Google Scholar
  17. 17.
    Shotton, J., Johnson, J., Cipolla, M.,, R.: Semantic texton forests for image categorization and segmentation. In: IEEE Computer Science Society International Conference of Computer Vision and Pattern Recognition, CVPR (2008)Google Scholar
  18. 18.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice-Hall, Inc, Upper Saddle River (2006)Google Scholar
  19. 19.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001)Google Scholar
  20. 20.
    Battiato, S., Farinella, G.M., Gallo, G., Ravì, D.: Scene categorization using bag of textons on spatial hierarchy. In: IEEE International Conference on Image Processing - ICIP 2008 (2008)Google Scholar
  21. 21.
    Farinella, G.M., Battiato, S., Gallo, G., Cipolla, R.: Natural Versus Artificial Scene Classification by Ordering Discrete Fourier Power Spectra. In: Proceedings of 12th International Workshop on Structural and Syntactic Pattern Recognition (SSPR)- Satellite event of the 19th International Conference of Pattern Recognition (ICPR). LNCS. Springer, Heidelberg (2008)Google Scholar
  22. 22.
    Battiato, S., Farinella, G.M., Gallo, G., Messina, E.: Classification of compressed images in constrained application domains. In: SPIE-IS&T 21th Annual Symposium Electronic Imaging Science and Technology 2009 - Digital Photography V (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Sebatiano Battiato
    • 1
  • Giovanni Maria Farinella
    • 1
  • Giovanni Gallo
    • 1
  • Daniele Ravì
    • 1
  1. 1.Image Processing LaboratoryUniversity of Catania, ITItaly

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