Fast Appearance Modeling for Automatic Primary Video Object Segmentation

Automatic segmentation of the primary object in a video clip is a challenging problem as there is no prior knowledge of the primary object. Most existing techniques thus adapt an iterative approach for foreground and background appearance modeling, i.e., fix the appearance model while optimizing the...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on image processing 2016-02, Vol.25 (2), p.503-515
Main Authors: Jiong Yang, Price, Brian, Xiaohui Shen, Zhe Lin, Junsong Yuan
Format: Article
Language:English
Subjects:
Adaptation models
appearance modeling
automatic
Automation
Consumption
Fields (mathematics)
graph cut
Graph theory
Image segmentation
Iterative methods
Markov random fields
Modelling
Motion segmentation
object
Object segmentation
Optimization
primary
Proposals
Segmentation
Transaction processing
video
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Automatic segmentation of the primary object in a video clip is a challenging problem as there is no prior knowledge of the primary object. Most existing techniques thus adapt an iterative approach for foreground and background appearance modeling, i.e., fix the appearance model while optimizing the segmentation and fix the segmentation while optimizing the appearance model. However, these approaches may rely on good initialization and can be easily trapped in local optimal. In addition, they are usually time consuming for analyzing videos. To address these limitations, we propose a novel and efficient appearance modeling technique for automatic primary video object segmentation in the Markov random field (MRF) framework. It embeds the appearance constraint as auxiliary nodes and edges in the MRF structure, and can optimize both the segmentation and appearance model parameters simultaneously in one graph cut. The extensive experimental evaluations validate the superiority of the proposed approach over the state-of-the-art methods, in both efficiency and effectiveness.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2015.2500820