Semi-supervised video object segmentation using multiple random walkers

Won Dong Jang; Chang-Su Kim

doi:10.5244/C.30.57

Semi-supervised video object segmentation using multiple random walkers

Won Dong Jang, Chang-Su Kim

Research output: Contribution to conference › Paper › peer-review

9 Citations (Scopus)

Abstract

A semi-supervised video object segmentation algorithm using multiple random walkers (MRW) is proposed in this work. We develop an initial probability estimation scheme that minimizes an objective function to roughly separate the foreground from the background. Then, we simulate MRW by employing the foreground and background agents. During the MRW process, we update restart distributions using a hybrid of inference restart rule and interactive restart rule. By performing these processes from the second to the last frames, we obtain a segment track of the target object. Furthermore, we optionally refine the segment track by performing Markov random field optimization. Experimental results demonstrate that the proposed algorithm significantly outperforms the state-of-the-art conventional algorithms on the SegTrack v2 dataset.

Original language	English
Pages	57.1-57.13
DOIs	https://doi.org/10.5244/C.30.57
Publication status	Published - 2016
Event	27th British Machine Vision Conference, BMVC 2016 - York, United Kingdom Duration: 2016 Sept 19 → 2016 Sept 22

Other

Other	27th British Machine Vision Conference, BMVC 2016
Country/Territory	United Kingdom
City	York
Period	16/9/19 → 16/9/22

Bibliographical note

Funding Information:
This work was supported partly by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. NRF-2015R1A2A1A10055037), and partly by the MSIP, Korea, under the ITRC support program supervised by the Institute for Information &communications Technology Promotion (No. IITP-2016-R2720-16-0007).

Funding Information:
This work was supported partly by the National Research F oundation of K orea (NRF) grant funded by the K orea go v ernment (MSIP) (No. NRF-2015R1A2A1A10055037), and partly by the MSIP , K orea, under the ITRC support program supervised by the Institute for Infor - mation &communications T echnology Promotion (No. IITP-2016-R2720-16-0007).

Publisher Copyright:
© 2016. The copyright of this document resides with its authors.

ASJC Scopus subject areas

Computer Vision and Pattern Recognition

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10.5244/C.30.57

Cite this

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title = "Semi-supervised video object segmentation using multiple random walkers",

abstract = "A semi-supervised video object segmentation algorithm using multiple random walkers (MRW) is proposed in this work. We develop an initial probability estimation scheme that minimizes an objective function to roughly separate the foreground from the background. Then, we simulate MRW by employing the foreground and background agents. During the MRW process, we update restart distributions using a hybrid of inference restart rule and interactive restart rule. By performing these processes from the second to the last frames, we obtain a segment track of the target object. Furthermore, we optionally refine the segment track by performing Markov random field optimization. Experimental results demonstrate that the proposed algorithm significantly outperforms the state-of-the-art conventional algorithms on the SegTrack v2 dataset.",

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Semi-supervised video object segmentation using multiple random walkers

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Bibliographical note

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