Robust interactive image segmentation via graph-based manifold ranking
Li H.1; Wu W.1; Wu E.1
Source PublicationComputational Visual Media
ISSN20960662 20960433
AbstractInteractive image segmentation aims at classifying the image pixels into foreground and background classes given some foreground and background markers. In this paper, we propose a novel framework for interactive image segmentation that builds upon graph-based manifold ranking model, a graph-based semi-supervised learning technique which can learn very smooth functions with respect to the intrinsic structure revealed by the input data. The final segmentation results are improved by overcoming two core problems of graph construction in traditional models: graph structure and graph edge weights. The user provided scribbles are treated as the must-link and must-not-link constraints. Then we model the graph as an approximatively k-regular sparse graph by integrating these constraints and our extended neighboring spatial relationships into graph structure modeling. The content and labels driven locally adaptive kernel parameter is proposed to tackle the insufficiency of previous models which usually employ a unified kernel parameter. After the graph construction, a novel three-stage strategy is proposed to get the final segmentation results. Due to the sparsity and extended neighboring relationships of our constructed graph and usage of superpixels, our model can provide nearly real-time, user scribble insensitive segmentations which are two core demands in interactive image segmentation. Last but not least, our framework is very easy to be extended to multi-label segmentation, and for some less complicated scenarios, it can even get the segmented object through single line interaction. Experimental results and comparisons with other state-of-the-art methods demonstrate that our framework can efficiently and accurately extract foreground objects from background.
KeywordGraph edge weights Graph structure Interactive image segmentation Manifold ranking Relevance inference
URLView the original
Fulltext Access
Citation statistics
Document TypeJournal article
CollectionUniversity of Macau
Affiliation1.Universidade de Macau
2.Chinese Academy of Sciences
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Li H.,Wu W.,Wu E.. Robust interactive image segmentation via graph-based manifold ranking[J]. Computational Visual Media,2015,1(3):183-195.
APA Li H.,Wu W.,&Wu E..(2015).Robust interactive image segmentation via graph-based manifold ranking.Computational Visual Media,1(3),183-195.
MLA Li H.,et al."Robust interactive image segmentation via graph-based manifold ranking".Computational Visual Media 1.3(2015):183-195.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Li H.]'s Articles
[Wu W.]'s Articles
[Wu E.]'s Articles
Baidu academic
Similar articles in Baidu academic
[Li H.]'s Articles
[Wu W.]'s Articles
[Wu E.]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Li H.]'s Articles
[Wu W.]'s Articles
[Wu E.]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.