Real-time superpixel segmentation by DBSCAN clustering algorithm

Shen, Jianbing, Hao, Xiaopeng, Liang, Zhiyuan, Liu, Yu, Wang, Wenguan and Shao, Ling (2016) Real-time superpixel segmentation by DBSCAN clustering algorithm. IEEE Transactions on Image Processing, 25 (12). pp. 5933-5942. ISSN 1057-7149

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Abstract

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.

Item Type: Article
Faculty \ School: Faculty of Science > School of Computing Sciences
Depositing User: Pure Connector
Date Deposited: 10 Mar 2017 01:41
Last Modified: 23 Oct 2020 23:58
URI: https://ueaeprints.uea.ac.uk/id/eprint/62934
DOI: 10.1109/TIP.2016.2616302

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