[1]ZENG Xianhua,YI Ronghui,HE Shanshan.Interactive image segmentation based on manifold ranking[J].CAAI Transactions on Intelligent Systems,2016,11(1):117-123.[doi:10.11992/tis.201505037]
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Interactive image segmentation based on manifold ranking

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 11 Number of periods: 2016 1 Page number: 117-123 Column: 学术论文—机器学习 Public date: 2016-02-25
Title:
Interactive image segmentation based on manifold ranking
Author(s):
ZENG Xianhua1; 2;  YI Ronghui1; 2;  HE Shanshan1; 2
1. College of Computer Science and Technology, Chongqing University of Posts and Telecommunication, Chongqing 400065, China;
2. Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunication, Chongqing 400065, China
Keywords:
manifold ranking; interactive image segmentation; saliency object; object detection; saliency map; background label; object label
CLC:
TP319.4
DOI:
10.11992/tis.201505037
Abstract:
The unsatisfactory results of salient object detection have made object segmentation difficult. In this study, we obtain accurate object segmentation by combining effective interactive information under the guidance of an initial saliency map. This method obtains the original saliency map using priori boundaries of the manifold ranking of the superpixels, and based on the obtained saliency map, marks the object labels of the non-salient object parts and the background labels of the salient background parts. Next, the superpixels for generating the new saliency map are re-sorted by manifold ranking with label information, and the newly obtained and original saliency maps are merged. Finally, object segmentation is achieved by adopting the adaptive threshold method for the merged saliency map. Image segmentation experiments with images from the Berkeley segmentation dataset (BSD) image database demonstrate that the proposed method can correctly segment objects from images and that the true positive rate (TPR) and the false positive rate (FPR) results are better than those achieved using the classical maximal similarity-based region merging (MSRM) image segmentation algorithm.
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