Outdoor Scene Image Segmentation Based on Background Recognition and Perceptual Organization

  • Authors:
  • Chang Cheng;Andreas Koschan;Chung-Hao Chen;David L. Page;Mongi A. Abidi

  • Affiliations:
  • Riverbed Technology, Sunnyvale, CA, USA;Imaging, Robotics, and Intelligent Systems Laboratory, Department of Electrical Engineering and Computer Science, The University of Tennessee, Knoxville, TN, USA;Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA, USA;Third Dimension Technologies LLC, Knoxville, TN, USA;Imaging, Robotics, and Intelligent Systems Laboratory, Department of Electrical Engineering and Computer Science, The University of Tennessee, Knoxville, TN, USA

  • Venue:
  • IEEE Transactions on Image Processing
  • Year:
  • 2012

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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.