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Scene classification in compressed and constrained domain

Scene classification in compressed and constrained domain

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Holistic representations of natural scenes are an effective and powerful source of information for semantic classification and analysis of images. Despite the technological hardware and software advances, consumer single-sensor imaging devices technology are quite far from the ability of recognising scenes and/or to exploit the visual content during (or after) acquisition time. 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. The authors exploit a holistic representation of the scene in the discrete cosine transform domain fully compatible with the JPEG format. The advised representation is coupled with a logistic classifier to perform classification of the scene at superordinate level of description (e.g. natural against artificial), or to discriminate between multiple classes of scenes usually acquired by a consumer imaging device (e.g. portrait, landscape and document). The proposed method is able to work in constrained domain. Experiments confirm the effectiveness of the proposed method. The obtained results closely match state-of-the-art methods in terms of accuracy outperforming in terms of computational resources.


    1. 1)
      • R. Lukac . (2008) Single-sensor imaging: methods and applications for digital cameras.
    2. 2)
      • Nikon Corporation: ‘Scene recognition system for more accurate autofocus, auto exposure and auto white balance’. Available at:, 2007.
    3. 3)
    4. 4)
      • Battiato, S., Mancuso, M., Bosco, A., Guarnera, M.: `Psychovisual and statistical optimization of quantization tables for DCT compression engines', IEEE Int. Conf. on Image Analysis and Processing, 2001, p. 602–606.
    5. 5)
    6. 6)
      • G.M. Farinella , S. Battiato , P.S.P. Wang . (2010) Representation models and machine learning techniques for scene classification, Pattern recognition and machine vision.
    7. 7)
    8. 8)
      • A. Bhattacharyya . On a measure of divergence between two statistical populations defined by probability distributions. Bull. Calcutta Math. Soc. , 99 - 109
    9. 9)
    10. 10)
    11. 11)
      • Farinella, G.M., Battiato, S., Gallo, G., Cipolla, R.: `Natural versus artificial scene classification by ordering discrete Fourier power spectra', Joint IAPR Int. Workshop on Structural, Syntactic, and Statistical Pattern Recognition, 2008, p. 137–146.
    12. 12)
      • S. Battiato , A.R. Bruna , G. Messina , G. Puglisi . (2010) Image processing for embedded devices.
    13. 13)
      • Battiato, S., Farinella, G.M., Gallo, G., Messina, E.: `Naturalness classification of images into DCT domain', SPIE – IS&T 21th Annual Symp. on Electronic Imaging Science and Technology – Digital Photography V, 2009, p. 1–12.
    14. 14)
    15. 15)
      • B. Shen , I.K. Sethi . Direct feature extraction from compressed images. Storage Retrieval Image Video Databases (SPIE) , 404 - 414
    16. 16)
      • Lazebnik, S., Schmid, C., Ponce, J.: `Beyond bags of features: spatial pyramid matching for recognizing natural scene categories', IEEE Int. Conf. on Computer Vision and Pattern Recognition, 2006, p. 2169–2178.
    17. 17)
      • A.R. Web . (2002) Statistical pattern recognition.
    18. 18)
      • Fei-Fei, L., Perona, P.: `A Bayesian hierarchical model for learning natural scene categories', IEEE Int. Conf. on Computer Vision and Pattern Recognition, 2005, p. 524–531.
    19. 19)
    20. 20)
    21. 21)
    22. 22)
      • Gorkani, M., Picard, R.: `Texture orientation for sorting photos at a glance', Int. Conf. on Pattern Recognition, 1994, p. 459–464.
    23. 23)
    24. 24)
    25. 25)
    26. 26)
      • Torralba, A., Pawan, S.: `Statistical context priming for object detection', Int. Conf. on Computer Vision, 2001, p. 763–770.
    27. 27)
    28. 28)
      • I. Biederman , Z. Pylyshyn . (1988) Aspects and extension of a theory of human image understanding, Computational processes in human vision: an interdisciplinary perspective.
    29. 29)
    30. 30)
    31. 31)
      • S. Battiato , A. Bosco , A.R. Bruna , R. Rizzo . Noise reduction for CFA image sensors exploiting HVS behavior. Sens. J. – MDPI Open Access – Special Issue on Integrated High-Performance Imagers , 3 , 1692 - 1713
    32. 32)
      • Turtinen, M., Pietikäinen, M.: `Visual training and classification of textured scene images', Int. Workshop on Texture Analysis and Synthesis, 2003, p. 101–106.
    33. 33)
      • Szummer, M., Picard, R.W.: `Indoor–outdoor image classification', IEEE Int. Workshop on Content-based Access of Image and Video Databases, 1998, p. 42–51.
    34. 34)
      • Torralba, A., Oliva, A.: `Semantic organization of scenes using discriminant structural templates', Int. Conf. on Computer Vision, 1999, p. 1253–1258.
    35. 35)
    36. 36)
    37. 37)
    38. 38)
      • D. Marr . (1982) Vision: a computational investigation into the human representation and processing of visual information.
    39. 39)
    40. 40)
    41. 41)

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