TY - GEN
T1 - Sequential clique optimization for video object segmentation
AU - Koh, Yeong Jun
AU - Lee, Young Yoon
AU - Kim, Chang-Su
N1 - 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.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Primary object segmentation
KW - Salient object detection
KW - Sequential clique optimization
KW - Video object segmentation
UR - http://www.scopus.com/inward/record.url?scp=85055709556&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-01264-9_32
DO - 10.1007/978-3-030-01264-9_32
M3 - Conference contribution
AN - SCOPUS:85055709556
SN - 9783030012632
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 537
EP - 556
BT - Computer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings
A2 - Ferrari, Vittorio
A2 - Sminchisescu, Cristian
A2 - Weiss, Yair
A2 - Hebert, Martial
PB - Springer Verlag
T2 - 15th European Conference on Computer Vision, ECCV 2018
Y2 - 8 September 2018 through 14 September 2018
ER -