Abstract
A novel algorithm to segment out objects in a video sequence is proposed in this work. First, we extract object instances in each frame. Then, we select a visually important object instance in each frame to construct the salient object track through the sequence. This can be formulated as finding the maximal weight clique in a complete k-partite graph, which is NP hard. Therefore, we develop the sequential clique optimization (SCO) technique to efficiently determine the cliques corresponding to salient object tracks. We convert these tracks into video object segmentation results. Experimental results show that the proposed algorithm significantly outperforms the state-of-the-art video object segmentation and video salient object detection algorithms on recent benchmark datasets.
Original language | English |
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Title of host publication | Computer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings |
Editors | Vittorio Ferrari, Cristian Sminchisescu, Yair Weiss, Martial Hebert |
Publisher | Springer Verlag |
Pages | 537-556 |
Number of pages | 20 |
ISBN (Print) | 9783030012632 |
DOIs | |
Publication status | Published - 2018 |
Event | 15th European Conference on Computer Vision, ECCV 2018 - Munich, Germany Duration: 2018 Sept 8 → 2018 Sept 14 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 11218 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Other
Other | 15th European Conference on Computer Vision, ECCV 2018 |
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Country/Territory | Germany |
City | Munich |
Period | 18/9/8 → 18/9/14 |
Bibliographical note
Funding Information:Acknowledgement. This work was supported partly by the Ministry of Science and ICT (MSIT), Korea, under the Information Technology Research Center (ITRC) support program (IITP-2018-2016-0-00464) supervised by the Institute for Information & communications Technology Promotion, and the National Research Foundations of Korea (NRF) grant funded by the Korea government (MSIP) (No. NRF-2015R1A2A1A10055037 and No. NRF-2018R1A2B3003896).
Publisher Copyright:
© 2018, Springer Nature Switzerland AG.
Keywords
- Primary object segmentation
- Salient object detection
- Sequential clique optimization
- Video object segmentation
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science