Primary object segmentation in videos based on region augmentation and reduction

Yeong Jun Koh, Chang-Su Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

61 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 languageEnglish
Title of host publicationProceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages7417-7425
Number of pages9
Volume2017-January
ISBN (Electronic)9781538604571
DOIs
Publication statusPublished - 2017 Nov 6
Event30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 - Honolulu, United States
Duration: 2017 Jul 212017 Jul 26

Other

Other30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
CountryUnited States
CityHonolulu
Period17/7/2117/7/26

ASJC Scopus subject areas

  • Signal Processing
  • Computer Vision and Pattern Recognition

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  • Cite this

    Koh, Y. J., & 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, pp. 7417-7425). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CVPR.2017.784