Real-time superpixel segmentation by DBSCAN clustering algorithm

Jianbing Shen, Xiaopeng Hao, Zhiyuan Liang, Yu Liu, Wenguan Wang, Ling Shao

Research output: Contribution to journalArticlepeer-review

261 Citations (Scopus)
75 Downloads (Pure)


In this paper, we propose a real-time image superpixel segmentation method with 50 frames/s by using the density-based spatial clustering of applications with noise (DBSCAN) algorithm. In order to decrease the computational costs of superpixel algorithms, we adopt a fast two-step framework. In the first clustering stage, the DBSCAN algorithm with color-similarity and geometric restrictions is used to rapidly cluster the pixels, and then, small clusters are merged into superpixels by their neighborhood through a distance measurement defined by color and spatial features in the second merging stage. A robust and simple distance function is defined for obtaining better superpixels in these two steps. The experimental results demonstrate that our real-time superpixel algorithm (50 frames/s) by the DBSCAN clustering outperforms the state-of-the-art superpixel segmentation methods in terms of both accuracy and efficiency.
Original languageEnglish
Pages (from-to)5933-5942
Number of pages10
JournalIEEE Transactions on Image Processing
Issue number12
Early online date11 Oct 2016
Publication statusPublished - 1 Dec 2016

Cite this