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Instant Scene Recognition on Mobile Platform

  • Sebastiano Battiato
  • Giovanni Maria Farinella
  • Mirko Guarnera
  • Daniele Ravì
  • Valeria Tomaselli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7585)

Abstract

Scene recognition is extremely useful to improve different tasks involved in the Image Generation Pipeline of single sensor mobile devices (e.g., white balancing, autoexposure, etc). This demo showcases our scene recognition engine implemented on a Nokia N900 smartphone. The engine exploits an image representation directly obtainable in the IGP of mobile devices. The demo works in realtime and it is able to discriminate among different classes of scenes. The framework is built by employing the FCam API to have an easy and precise control of the mobile digital camera. Each acquired image (or frame of a video) is holistically represented starting from the statistics collected on DCT domain. This allow instant and “free of charge” feature extraction process since the DCT is always computed into the IGP of a mobile for storage purposes (i.e., JPEG or MPEG format). A SVM classifier is used to perform the final inference about the context of the scene.

Keywords

Scene Recognition DCT Features FCam Mobile Platform 

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References

  1. 1.
    Bianco, S., Ciocca, G., Cusano, C., Schettini, R.: Improving color constancy using indoor - outdoor image classification. IEEE Transactions on Image Processing 17, 2381–2392 (2008)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Farinella, G.M., Battiato, S.: Scene classification in compressed and constrained domain. IET Computer Vision 5, 320–334 (2011)CrossRefGoogle Scholar
  3. 3.
    Battiato, S., Farinella, G.M., Gallo, G., Ravì, D.: Exploiting textons distributions on spatial hierarchy for scene classification. Journal on Image and Video Processing 2010, 7:1–7:13 (2010)Google Scholar
  4. 4.
    Lam, E., Goodman, J.: A mathematical analysis of the dct coefficient distributions for images. IEEE Transactions on Image Processing 9, 1661–1666 (2000)zbMATHCrossRefGoogle Scholar
  5. 5.
    Adams, A., Talvala, E.V., Park, S.H., Jacobs, D.E., Ajdin, B., Gelfand, N., Dolson, J., Vaquero, D., Baek, J., Tico, M., Lensch, H.P.A., Matusik, W., Pulli, K., Horowitz, M., Levoy, M.: The frankencamera: an experimental platform for computational photography. ACM Transactions on Graphics - Proceedings of ACM SIGGRAPH 2010 29, 29:1–29:12 (2010)Google Scholar
  6. 6.
    FCam Garage: FCam API (2012), http://fcam.garage.maemo.org/
  7. 7.
    Willow Garage: OpenCV: Open source computer vision library (2012), http://opencv.willowgarage.com/wiki/
  8. 8.
    Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines (2012), http://www.csie.ntu.edu.tw/~cjlin/libsvm/
  9. 9.
    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)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Sebastiano Battiato
    • 1
  • Giovanni Maria Farinella
    • 1
  • Mirko Guarnera
    • 2
  • Daniele Ravì
    • 1
  • Valeria Tomaselli
    • 2
  1. 1.Image Processing LaboratoryUniversity of CataniaItaly
  2. 2.Advanced System TechnologySTMicroelectronicsCataniaItaly

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