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Erschienen in:
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2018 | OriginalPaper | Buchkapitel

Sequential Clique Optimization for Video Object Segmentation

verfasst von : Yeong Jun Koh, Young-Yoon Lee, Chang-Su Kim

Erschienen in: Computer Vision – ECCV 2018

Verlag: Springer International Publishing

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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.

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Vorheriges Kapitel HBE: Hand Branch Ensemble Network for Real-Time 3D Hand Pose Estimation
Nächstes Kapitel Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network
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Metadaten
Titel
Sequential Clique Optimization for Video Object Segmentation
verfasst von
Yeong Jun Koh
Young-Yoon Lee
Chang-Su Kim
Copyright-Jahr
2018
Buch
Computer Vision – ECCV 2018

Print ISBN: 978-3-030-01263-2

Electronic ISBN: 978-3-030-01264-9

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-030-01264-9_32