Primary object segmentation in videos based on region augmentation and reduction

Yeong Jun Koh; Chang-Su Kim

doi:10.1109/CVPR.2017.784

Primary object segmentation in videos based on region augmentation and reduction

Yeong Jun Koh, Chang-Su Kim

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

74 Citations (Scopus)

Abstract

A novel algorithm to segment a primary object in a video sequence is proposed in this work. First, we generate candidate regions for the primary object using both color and motion edges. Second, we estimate initial primary object regions, by exploiting the recurrence property of the primary object. Third, we augment the initial regions with missing parts or reducing them by excluding noisy parts repeatedly. This augmentation and reduction process (ARP) identifies the primary object region in each frame. Experimental results demonstrate that the proposed algorithm significantly outperforms the state-of-the-Art conventional algorithms on recent benchmark datasets.

Original language	English
Title of host publication	Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	7417-7425
Number of pages	9
Volume	2017-January
ISBN (Electronic)	9781538604571
DOIs	https://doi.org/10.1109/CVPR.2017.784
Publication status	Published - 2017 Nov 6
Event	30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 - Honolulu, United States Duration: 2017 Jul 21 → 2017 Jul 26

Other

Other	30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
Country	United States
City	Honolulu
Period	17/7/21 → 17/7/26

ASJC Scopus subject areas

Signal Processing
Computer Vision and Pattern Recognition

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Primary object segmentation in videos based on region augmentation and reduction. / Koh, Yeong Jun; Kim, Chang-Su.

Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. Vol. 2017-January Institute of Electrical and Electronics Engineers Inc., 2017. p. 7417-7425.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Koh, YJ & Kim, C-S 2017, Primary object segmentation in videos based on region augmentation and reduction. in Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 7417-7425, 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, United States, 17/7/21. https://doi.org/10.1109/CVPR.2017.784

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AB - A novel algorithm to segment a primary object in a video sequence is proposed in this work. First, we generate candidate regions for the primary object using both color and motion edges. Second, we estimate initial primary object regions, by exploiting the recurrence property of the primary object. Third, we augment the initial regions with missing parts or reducing them by excluding noisy parts repeatedly. This augmentation and reduction process (ARP) identifies the primary object region in each frame. Experimental results demonstrate that the proposed algorithm significantly outperforms the state-of-the-Art conventional algorithms on recent benchmark datasets.

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Primary object segmentation in videos based on region augmentation and reduction

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