Paper Info
Title
A pipeline using multi-layer Tumors Automata for interactive multi-label image segmentation
Abstract
In this paper, we investigate a novel algorithm to the problem of interactive image segmentation. We propose an extension of the Growcut framework using the Tumors Automata (TA) formed from the superpixel. The proposed TA is similar to Cellular Automata but can directly deal with superpixel. The superpixels (image segments) can provide powerful boundary cues to guide segmentation, where superpixels can be collected easily by over-segmenting the image using any reasonable existing segmentation algorithms. Given a small number of user-labelled superpixels, the rest of the image is segmented automatically by a TA. When the automaton labels the image, the segmentation evolution is faster than Growcut because of the iterative process. Moreover, a level set method and multi-layer TA are employed to further improve the performance. Experiments conducted on the Berkeley Segmentation Database demonstrate the superior performance of our method over the state-of-the-art methods.
Year
DOI
Venue
2016
10.1109/HSI.2016.7529648
2016 9th International Conference on Human System Interactions (HSI)
Keywords
Field
DocType
multilayer tumor automata,interactive multilabel image segmentation,Growcut framework,cellular automata,user-labelled superpixel,level set method,multilayer TA
Computer vision,Scale-space segmentation,Pattern recognition,Image texture,GrowCut algorithm,Computer science,Segmentation,Segmentation-based object categorization,Image segmentation,Feature extraction,Artificial intelligence,Minimum spanning tree-based segmentation
Conference
ISSN
ISBN
Citations 
2158-2246
978-1-5090-1730-0
0
PageRank 
References 
Authors
0.34
11
4
Name
Order
Citations
PageRank
Sixian Chan1127.69
Xiaolong Zhou210319.67
Zhuo Zhang318627.49
Sheng-Yong Chen41077114.06