Bilateral Video Object Segmentation Using Dynamic Appearance Modeling and Higher-Order Potential

Aiming at the problems of poor quality and low time efficiency of video object segmentation in complex scenes, this paper proposes a novel bilateral video object segmentation using dynamic appearance modeling and higher-order potential, which is formulated the video object segmentation problem as bi...

Full description

Saved in:
Bibliographic Details
Published inJisuanji kexue yu tansuo Vol. 14; no. 12; pp. 2108 - 2121
Main Author TIAN Ying, GUI Yan, XIONG Daming
Format Journal Article
LanguageChinese
Published Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press 01.12.2020
Subjects
bilateral grid
bilateral space
confidence-based dynamic appearance model
higher-order energy potential
video object segmentation
Online AccessGet full text

Cover

More Information
Summary:Aiming at the problems of poor quality and low time efficiency of video object segmentation in complex scenes, this paper proposes a novel bilateral video object segmentation using dynamic appearance modeling and higher-order potential, which is formulated the video object segmentation problem as binary labeling of Markov random field (MRF) based on bilateral grid cells. Firstly, this paper resamples each pixel in the video sequence with labeled keyframes into a higher-dimensional bilateral grid, which greatly reduces the video data to be processed. Secondly, this paper constructs a graph-cut optimization model using non-empty grid cells as the nodes of graph. The key is to construct the dynamic appearance model with confidence measurements, and to introduce a robust higher-order potential into the energy function. Finally, this paper uses the max-flow/min-cut algorithm to solve the global optimization problem, and binary label assignment of each pixel is achieved to obtain the high-quality video object segme
ISSN:1673-9418
DOI:10.3778/j.issn.1673-9418.1911014