Segmentation for Indoor Scenes Based on DBSCAN Clustering

Aiming at the challenging problems of RGB-D images with rich 3D geometric features and high com- plexity, this paper proposes a segmentation algorithm for RGB-D images of indoor scenes. Firstly, generating superpixels by over-segmentation of RGB-D images and measuring the similarity of two superpixels based on the distance. Then, the DBSCAN algorithm is used to cluster the superpixels with similar color and geo- metric information into the same classification. In the clustering process, we restrict the diffusion area to reduce computational complexity. A lot of experimental results on the database of RGB-D images show that the segmentation accuracy and rate of our algorithm exceed the other algorithms, which proves our algo- rithm’s efficiency and accuracy.