Non-local Characterization of Scenery Images: Statistics, 3D Reasoning, and a Generative Model

  • Tamar Avraham
  • Michael Lindenbaum
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6315)


This work focuses on characterizing scenery images. We semantically divide the objects in natural landscape scenes into background and foreground and show that the shapes of the regions associated with these two types are statistically different. We then focus on the background regions. We study statistical properties such as size and shape, location and relative location, the characteristics of the boundary curves and the correlation of the properties to the region’s semantic identity. Then we discuss the imaging process of a simplified 3D scene model and show how it explains the empirical observations. We further show that the observed properties suffice to characterize the gist of scenery images, propose a generative parametric graphical model, and use it to learn and generate semantic sketches of new images, which indeed look like those associated with natural scenery.


Natural Image Background Region Land Region Foreground Object Aerial Image 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Tamar Avraham
    • 1
  • Michael Lindenbaum
    • 1
  1. 1.Computer science departmentTechnion - I.I.T.HaifaIsrael

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