IEICE TRANSACTIONS on Information
An Enhanced Affinity Graph for Image Segmentation
Summary :
This paper proposes an enhanced affinity graph (EA-graph) for image segmentation. Firstly, the original image is over-segmented to obtain several sets of superpixels with different scales, and the color and texture features of the superpixels are extracted. Then, the similarity relationship between neighborhood superpixels is used to construct the local affinity graph. Meanwhile, the global affinity graph is obtained by sparse reconstruction among all superpixels. The local affinity graph and global affinity graph are superimposed to obtain an enhanced affinity graph for eliminating the influences of noise and isolated regions in the image. Finally, a bipartite graph is introduced to express the affiliation between pixels and superpixels, and segmentation is performed using a spectral clustering algorithm. Experimental results on the Berkeley segmentation database demonstrate that our method achieves significantly better performance compared to state-of-the-art algorithms.
- Publication
- IEICE TRANSACTIONS on Information Vol.E102-D No.5 pp.1073-1080
- Publication Date
- 2019/05/01
- Publicized
- 2019/02/04
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2018EDP7322
- Type of Manuscript
- PAPER
- Category
- Image Processing and Video Processing
Authors
Guodong SUN
Hubei University of Technology
Kai LIN
Hubei University of Technology
Junhao WANG
Hubei University of Technology
Yang ZHANG
Nanjing University
Keyword
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Guodong SUN, Kai LIN, Junhao WANG, Yang ZHANG, "An Enhanced Affinity Graph for Image Segmentation" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 5, pp. 1073-1080, May 2019, doi: 10.1587/transinf.2018EDP7322.
Abstract: This paper proposes an enhanced affinity graph (EA-graph) for image segmentation. Firstly, the original image is over-segmented to obtain several sets of superpixels with different scales, and the color and texture features of the superpixels are extracted. Then, the similarity relationship between neighborhood superpixels is used to construct the local affinity graph. Meanwhile, the global affinity graph is obtained by sparse reconstruction among all superpixels. The local affinity graph and global affinity graph are superimposed to obtain an enhanced affinity graph for eliminating the influences of noise and isolated regions in the image. Finally, a bipartite graph is introduced to express the affiliation between pixels and superpixels, and segmentation is performed using a spectral clustering algorithm. Experimental results on the Berkeley segmentation database demonstrate that our method achieves significantly better performance compared to state-of-the-art algorithms.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDP7322/_p
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@ARTICLE{e102-d_5_1073,
author={Guodong SUN, Kai LIN, Junhao WANG, Yang ZHANG, },
journal={IEICE TRANSACTIONS on Information},
title={An Enhanced Affinity Graph for Image Segmentation},
year={2019},
volume={E102-D},
number={5},
pages={1073-1080},
abstract={This paper proposes an enhanced affinity graph (EA-graph) for image segmentation. Firstly, the original image is over-segmented to obtain several sets of superpixels with different scales, and the color and texture features of the superpixels are extracted. Then, the similarity relationship between neighborhood superpixels is used to construct the local affinity graph. Meanwhile, the global affinity graph is obtained by sparse reconstruction among all superpixels. The local affinity graph and global affinity graph are superimposed to obtain an enhanced affinity graph for eliminating the influences of noise and isolated regions in the image. Finally, a bipartite graph is introduced to express the affiliation between pixels and superpixels, and segmentation is performed using a spectral clustering algorithm. Experimental results on the Berkeley segmentation database demonstrate that our method achieves significantly better performance compared to state-of-the-art algorithms.},
keywords={},
doi={10.1587/transinf.2018EDP7322},
ISSN={1745-1361},
month={May},}
Copy
TY - JOUR
TI - An Enhanced Affinity Graph for Image Segmentation
T2 - IEICE TRANSACTIONS on Information
SP - 1073
EP - 1080
AU - Guodong SUN
AU - Kai LIN
AU - Junhao WANG
AU - Yang ZHANG
PY - 2019
DO - 10.1587/transinf.2018EDP7322
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2019
AB - This paper proposes an enhanced affinity graph (EA-graph) for image segmentation. Firstly, the original image is over-segmented to obtain several sets of superpixels with different scales, and the color and texture features of the superpixels are extracted. Then, the similarity relationship between neighborhood superpixels is used to construct the local affinity graph. Meanwhile, the global affinity graph is obtained by sparse reconstruction among all superpixels. The local affinity graph and global affinity graph are superimposed to obtain an enhanced affinity graph for eliminating the influences of noise and isolated regions in the image. Finally, a bipartite graph is introduced to express the affiliation between pixels and superpixels, and segmentation is performed using a spectral clustering algorithm. Experimental results on the Berkeley segmentation database demonstrate that our method achieves significantly better performance compared to state-of-the-art algorithms.
ER -
English
