NDLI: Video segmentation scheme based on AMC

Content Provider	IET Digital Library
Author	Cao, Ying Sun, Lijuan Han, Chong Guo, Jian
Abstract	Video segmentation has become a fundamental of various multimedia applications. Spatiotemporal coherence is important for video segmentation. In this study, to balance the spatiotemporal coherence in scenes with deformation or large motion, the authors propose a novel segmentation scheme based on the absorbing Markov chain (AMC) model named directed graph segmentation based on AMC. In their study, they first generate primary proposals per frame. Then, they train weight models by using a part of primary proposals with their features and feature scores. Next, they construct a directed AMC graph, in which states are the generated primary proposals and edge weights are decided by trained weight models. They subsequently perform the first proposal selection per frame by thresholding the modified absorbed time. Afterwards, they design a reselection algorithm to filter the selected proposals and ensure the proposals, which are the most likely to be the motion object in each frame, to be selected as candidates. Finally, they employ the graph-cuts based optimisation algorithm to generate refined per pixel segmentation by using object and background models built by candidate proposals under the concept of Gaussian mixture models. Experimental results demonstrate that the proposed scheme shows competitive performance compared with advanced algorithms.
Starting Page	407
Ending Page	416
Page Count	10
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 3, Feb (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.6659
Journal	IET Image Processing
Publisher Date	2019-05-14
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Background Model Candidate Proposals Combinatorial Mathematics Computer Vision And Image Processing Technique Deformation Directed AMC Graph Directed Graph Edge Weights Feature Scores Gaussian Mixture Model Gaussian Processes Graph Segmentation Graph-cuts Based Optimisation Algorithm Image Segmentation Markov Chain Model Markov Processes Mixture Model Modified Absorbed Time Motion Object Multimedia Application Optical, Image And Video Signal Processing Optimisation Optimisation Technique Pixel Segmentation Proposal Selection Spatiotemporal Coherence Trained Weight Model Video Segmentation Scheme Video Signal Processing
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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