Back to Search
Start Over
Adaptive Nonlocal Random Walks for Image Superpixel Segmentation.
- Source :
-
IEEE Transactions on Circuits & Systems for Video Technology . Mar2020, Vol. 30 Issue 3, p822-834. 13p. - Publication Year :
- 2020
-
Abstract
- In this paper, we propose a novel superpixel segmentation method using an adaptive nonlocal random walk (ANRW) algorithm. There are three main steps in our image superpixel segmentation algorithm. Our method is based on the random walk model, in which the seed points are produced to generate the initial superpixels by a gradient-based method in the first step. In the second step, the ANRW is proposed to get the initial superpixels by adjusting the NRW to obtain a better image and superpixel segmentation. In the last step, these small superpixels are merged to get the final regular and compact superpixels. The experimental results demonstrate that our method achieves a better superpixel performance than the state-of-the-art methods. Our source code will be available at: http://github.com/shenjianbing/ANRW. [ABSTRACT FROM AUTHOR]
- Subjects :
- *RANDOM walks
*IMAGE segmentation
*IMAGE color analysis
*ALGORITHMS
*SOURCE code
Subjects
Details
- Language :
- English
- ISSN :
- 10518215
- Volume :
- 30
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Circuits & Systems for Video Technology
- Publication Type :
- Academic Journal
- Accession number :
- 143312880
- Full Text :
- https://doi.org/10.1109/TCSVT.2019.2896438