Paper Info
Title
Efficient Video Segmentation Using Parametric Graph Partitioning
Abstract
Video segmentation is the task of grouping similar pixels in the spatio-temporal domain, and has become an important preprocessing step for subsequent video analysis. Most video segmentation and supervoxel methods output a hierarchy of segmentations, but while this provides useful multiscale information, it also adds difficulty in selecting the appropriate level for a task. In this work, we propose an efficient and robust video segmentation framework based on parametric graph partitioning (PGP), a fast, almost parameter free graph partitioning method that identifies and removes between-cluster edges to form node clusters. Apart from its computational efficiency, PGP performs clustering of the spatio-temporal volume without requiring a pre-specified cluster number or bandwidth parameters, thus making video segmentation more practical to use in applications. The PGP framework also allows processing sub-volumes, which further improves performance, contrary to other streaming video segmentation methods where sub-volume processing reduces performance. We evaluate the PGP method using the SegTrack v2 and Chen Xiph.org datasets, and show that it outperforms related state-of-the-art algorithms in 3D segmentation metrics and running time.
Year
DOI
Venue
2015
10.1109/ICCV.2015.361
ICCV '15 Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV)
Field
DocType
Volume
Computer vision,Scale-space segmentation,Pattern recognition,Segmentation,Computer science,Segmentation-based object categorization,Determining the number of clusters in a data set,Image segmentation,Parametric statistics,Artificial intelligence,Graph partition,Cluster analysis
Conference
2015
Issue
ISSN
Citations 
1
1550-5499
6
PageRank 
References 
Authors
0.41
26
4
Name
Order
Citations
PageRank
Chen-Ping Yu1533.87
hieu le281.12
Gregory J. Zelinsky318320.64
Dimitris Samaras41740101.49