It is difficult to obtain accurate segmentation results for a color map when there are shadows, low-contrast edges, or blurred regions in the image. The depth discontinuity of the image provides useful information for the identification of object boundaries. In this paper, a color and depth image (RGB-D) image segmentation method based on superpixels and multi-feature fusion graph theory is proposed. The method consists of two stages: (1) superpixel segmentation stage and (2) graph-based superpixel merge stage. Color information and depth information are combined to establish a Euclidean distance metric for the pixels, and superpixels are obtained through iterative clustering. A multi-feature fusion adjacent superpixel similarity measurement method based on the RGB-D data is proposed. A graph-based energy function is established, and a label cost is introduced in the energy function to eliminate redundant labels. We carried out many experiments on the RGB-D image database, and the results show that our superpixel segmentation method and graph-based superpixel merging method not only have higher segmentation accuracy than the existing methods, but also have advantages in terms of running time and memory consumption.

中文翻译：

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使用超像素和多特征融合图论的RGB-D图像分割

当图像中存在阴影、低对比度边缘或模糊区域时，很难对彩色图获得准确的分割结果。图像的深度不连续性为识别对象边界提供了有用的信息。本文提出了一种基于超像素和多特征融合图论的颜色和深度图像（RGB-D）图像分割方法。该方法由两个阶段组成：（1）超像素分割阶段和（2）基于图的超像素合并阶段。结合颜色信息和深度信息为像素建立欧几里德距离度量，通过迭代聚类获得超像素。提出了一种基于RGB-D数据的多特征融合相邻超像素相似度测量方法。建立基于图的能量函数，并且在能量函数中引入标签成本以消除冗余标签。我们在RGB-D图像数据库上进行了多次实验，结果表明我们的超像素分割方法和基于图的超像素合并方法不仅比现有方法具有更高的分割精度，而且在运行时间和内存消耗。