Riverbed Technology, Sunnyvale, CA 94085, USA.
IEEE Trans Image Process. 2012 Mar;21(3):1007-19. doi: 10.1109/TIP.2011.2169268. Epub 2011 Sep 23.
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.
在本文中，我们提出了一种新颖的基于背景识别和感知组织的户外场景图像分割算法。我们基于颜色和纹理信息识别背景对象，如天空、地面和植被。对于结构上具有挑战性的物体，它们通常由多个组成部分组成，我们开发了一种感知组织模型，可以捕捉组成部分之间的非偶然结构关系，并相应地将它们组合在一起，而不依赖于特定物体的先验知识。我们的实验结果表明，我们提出的方法在两个具有挑战性的户外数据库（Gould 数据集和 Berkeley 分割数据集）上优于两种最先进的图像分割方法，并在各种户外自然场景环境中实现了准确的分割质量。
