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Sequential Clique Optimization for Video Object Segmentation

  • Yeong Jun KohEmail author
  • Young-Yoon Lee
  • Chang-Su Kim
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11218)

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.

Keywords

Video object segmentation Primary object segmentation Salient object detection Sequential clique optimization 

Notes

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).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Electrical EngineeringKorea UniversitySeoulKorea
  2. 2.Samsung Electronics Co., Ltd.SeoulKorea

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