Abstract
In this paper, we propose a novel outdoor scene image segmentation algorithm based on background recognition and perceptual organization. We recognize the background objects such as the sky, the ground, and vegetation based on the color and texture information. For the structurally challenging objects, which usually consist of multiple constituent parts, we developed a perceptual organization model that can capture the nonaccidental structural relationships among the constituent parts of the structured objects and, hence, group them together accordingly without depending on a priori knowledge of the specific objects. Our experimental results show that our proposed method outperformed two state-of-the-art image segmentation approaches on two challenging outdoor databases (Gould data set and Berkeley segmentation data set) and achieved accurate segmentation quality on various outdoor natural scene environments.
| Original language | English |
|---|---|
| Article number | 6025295 |
| Pages (from-to) | 1007-1019 |
| Number of pages | 13 |
| Journal | IEEE Transactions on Image Processing |
| Volume | 21 |
| Issue number | 3 |
| DOIs | |
| State | Published - Mar 2012 |
Funding
Manuscript received August 17, 2010; revised April 16, 2011 and July 22, 2011; accepted August 16, 2011 Date of publication September 22, 2011; date of current version February 17, 2012. This work was supported in part by the University Research Program in Robotics under Grant DOE-DE-FG52-2004NA25589 and in part by the U.S. Air Force under Grant FA8650-10-1-5902. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Xilin Chen.
Keywords
- Boundary energy
- image segmentation
- perceptual organization