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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Vydané v:IEEE transactions on image processing Ročník 25; číslo 12; s. 5933 - 5942
Hlavní autori: Shen, Jianbing, Hao, Xiaopeng, Liang, Zhiyuan, Liu, Yu, Wang, Wenguan, Shao, Ling
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.12.2016
Predmet:
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
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2016.2616302