Auto-Marking Image Segmentation Based Manifold Ranking

DOI

10.2991/aiie-15.2015.13How to use a DOI?

Keywords

image segmentation; manifold ranking; saliency; background prior; auto-marking

Abstract

Interactive image segmentation requires adjusting label information manually which will lead to tedious marking process. We propose an auto-marking image segmentation method. It can obtain the object prior and background prior automatically. We segment an image into superpixels (regions) and get the object saliency map via the manifold ranking in the guide of background prior information, then we choose part of the superpixels with higher saliency values as object marked seeds, and select the background marked seeds with the combination of background prior and the result of manifold ranking, thus obtaining the final image segmentation through maximal similarity based region merging. Experimental results on images with single object and similar adjacent objects show that the proposed algorithm can automatically add the correct label information, and can obtain segmentation accuracy that is better than saliency-seeded region merging (SSRMf) algorithm, while is more convenient than interactive segmentation by avoiding manual operations.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - X.H. Zeng
AU  - R.H. Yi
AU  - S.W. Zhu
AU  - S.S. He
PY  - 2015/07
DA  - 2015/07
TI  - Auto-Marking Image Segmentation Based Manifold Ranking
BT  - Proceedings of the 2015 International Conference on Artificial Intelligence and Industrial Engineering
PB  - Atlantis Press
SP  - 45
EP  - 48
SN  - 1951-6851
UR  - https://doi.org/10.2991/aiie-15.2015.13
DO  - 10.2991/aiie-15.2015.13
ID  - Zeng2015/07
ER  -