Sciweavers

Share
ECCV
2010
Springer

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

3 years 8 months ago
Non-local Characterization of Scenery Images: Statistics, 3D Reasoning, and a Generative Model
Abstract. 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.
Tamar Avraham, Michael Lindenbaum
Added 10 Feb 2011
Updated 10 Feb 2011
Type Journal
Year 2010
Where ECCV
Authors Tamar Avraham, Michael Lindenbaum
Comments (0)
books