Natural Versus Artificial Scene Classification by Ordering Discrete Fourier Power Spectra

  • Giovanni Maria Farinella
  • Sebastiano Battiato
  • Giovanni Gallo
  • Roberto Cipolla
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5342)

Abstract

Holistic representations of natural scenes is an effective and powerful source of information for semantic classification and analysis of arbitrary images. Recently, the frequency domain has been successfully exploited to holistically encode the content of natural scenes in order to obtain a robust representation for scene classification. In this paper, we present a new approach to naturalness classification of scenes using frequency domain. The proposed method is based on the ordering of the Discrete Fourier Power Spectra. Features extracted from this ordering are shown sufficient to build a robust holistic representation for Natural vs. Artificial scene classification. Experiments show that the proposed frequency domain method matches the accuracy of other state-of-the-art solutions.

Keywords

Power Spectrum Frequency Domain Visual Word Natural Scene Scene Category 
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.

References

  1. 1.
    Oliva, A., Torralba, A.: Building the gist of a scene: The role of global image features in recognition. Visual Perception, Progress in Brain Research, 251–256 (2006)Google Scholar
  2. 2.
    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
  3. 3.
    Renninger, L.W., Malik, J.: When is scene recognition just texture recognition? Vision Research 44, 2301–2311 (2004)CrossRefGoogle Scholar
  4. 4.
    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)CrossRefMATHGoogle Scholar
  5. 5.
    Bosch, A., Zisserman, A., Munoz, X.: Scene classification via pLSA. In: Proceedings of the European Conference on Computer Vision (2006)Google Scholar
  6. 6.
    Torralba, A., Oliva, A.: Statistics of natural image categories. Network: Computing in Neural Systems 14, 391–412 (2003)CrossRefGoogle Scholar
  7. 7.
    Torralba, A., Oliva, A.: Depth estimation from image structure. IEEE Trans. Pattern Anal. Mach. Intell. 24(9), 1226–1238 (2002)CrossRefMATHGoogle Scholar
  8. 8.
    Torralba, A., Pawan, S.: Statistical context priming for object detection. In: Internation Conference on Computer Vision (2001)Google Scholar
  9. 9.
    Belongie, S., Malik, J., Puzicha, J.: Shape context: A new descriptor for shape matching and object recognition. In: Neural Information Processing Systems (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Giovanni Maria Farinella
    • 1
  • Sebastiano Battiato
    • 1
  • Giovanni Gallo
    • 1
  • Roberto Cipolla
    • 2
  1. 1.ITUniversity of CataniaItaly
  2. 2.University of CambridgeUK

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