Paper Info
Title
Class-Specified segmentation with multi-scale superpixels
Abstract
This paper proposes a class-specified segmentation method, which can not only segment foreground objects from background at pixel level, but also parse images. Such class-specified segmentation is very helpful to many other computer vision tasks including computational photography. The novelty of our method is that we use multi-scale superpixels to effectively extract object-level regions instead of using only single scale superpixels. The contextual information across scales and the spatial coherency of neighboring superpixels in the same scale are represented and integrated via a Conditional Random Field model on multi-scale superpixels. Compared with the other methods that have ever used multi-scale superpixel extraction together with across-scale contextual information modeling, our method not only has fewer free parameters but also is simpler and effective. The superiority of our method, compared with related approaches, is demonstrated on the two widely used datasets of Graz02 and MSRC.
Year
DOI
Venue
2012
10.1007/978-3-642-37410-4_14
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Keywords
Field
DocType
single scale superpixels,multi-scale superpixel extraction,class-specified segmentation,multi-scale superpixels,class-specified segmentation method,neighboring superpixels,contextual information,across-scale contextual information modeling,computational photography,conditional random field model
Conditional random field,Information theory,Computer vision,Color photography,Stochastic gradient descent,Pattern recognition,Computer science,Segmentation,Computational photography,Artificial intelligence,Pixel,Parsing
Conference
Volume
Issue
ISSN
7728 LNCS
PART 1
16113349
Citations 
PageRank 
References 
0
0.34
13
Authors
4
Name
Order
Citations
PageRank
Han Liu119028.96
Yanyun Qu221638.66
Yang Wu38418.42
Hanzi Wang4110792.85