Real-Time Superpixel Segmentation by DBSCAN Clustering Algorithm

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 f...

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Bibliographic Details
Published in:IEEE transactions on image processing Vol. 25; no. 12; pp. 5933 - 5942
Main Authors: Shen, Jianbing, Hao, Xiaopeng, Liang, Zhiyuan, Liu, Yu, Wang, Wenguan, Shao, Ling
Format: Journal Article
Language:English
Published: United States IEEE 01.12.2016
Subjects:
Clustering algorithms
Computational efficiency
DBSCAN
Image color analysis
Image segmentation
Optimization
Real-time
Real-time systems
segmentation
Shape
superpixel
ISSN:1057-7149, 1941-0042, 1941-0042
Online Access:Get full text
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Summary: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.
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2016.2616302