TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Higher-Order Conditional Random Field established with CNNs for Video Object Segmentation |
| 영문제목(English Title) |
Higher-Order Conditional Random Field established with CNNs for Video Object Segmentation |
| 저자(Author) |
Chuanyan Hao
Yuqi Wang
Bo Jiang
Sijiang Liu
Zhi-Xin Yang
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| 원문수록처(Citation) |
VOL 15 NO. 09 PP. 3204 ~ 3220 (2021. 09) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
We perform the task of video object segmentation by incorporating a conditional random field (CRF) and convolutional neural networks (CNNs). Most methods employ a CRF to refine a coarse output from fully convolutional networks. Others treat the inference process of the CRF as a recurrent neural network and then combine CNNs and the CRF into an end-to-end model for video object segmentation. In contrast to these methods, we propose a novel higher-order CRF model to solve the problem of video object segmentation. Specifically, we use CNNs to establish a higher-order dependence among pixels, and this dependence can provide critical global information for a segmentation model to enhance the global consistency of segmentation. In general, the optimization of the higher-order energy is extremely difficult. To make the problem tractable, we decompose the higher-order energy into two parts by utilizing auxiliary variables and then solve it by using an iterative process. We conduct quantitative and qualitative analyses on multiple datasets, and the proposed method achieves competitive results. |
| 키워드(Keyword) |
Video object segmentation
Conditional random field
Convolution Neural Networks
Higher-order potential
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| 파일첨부 |
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