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Saliency detection via robust seed selection of foreground and background priors

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

Standard

Saliency detection via robust seed selection of foreground and background priors. / Jian, Muwei; Wang, Ruihong; Yu, Hui; Dong, Junyu; Wang, Yujuan; Yin, Yilong; Lam, Kin-Man.

2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2020. p. 797-801 (IEEE APSIPA ASC Proceedings Series).

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

Harvard

Jian, M, Wang, R, Yu, H, Dong, J, Wang, Y, Yin, Y & Lam, K-M 2020, Saliency detection via robust seed selection of foreground and background priors. in 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE APSIPA ASC Proceedings Series, IEEE, pp. 797-801, 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, Lanzhou, China, 18/11/19. https://doi.org/10.1109/APSIPAASC47483.2019.9023263

APA

Jian, M., Wang, R., Yu, H., Dong, J., Wang, Y., Yin, Y., & Lam, K-M. (2020). Saliency detection via robust seed selection of foreground and background priors. In 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) (pp. 797-801). (IEEE APSIPA ASC Proceedings Series). IEEE. https://doi.org/10.1109/APSIPAASC47483.2019.9023263

Vancouver

Jian M, Wang R, Yu H, Dong J, Wang Y, Yin Y et al. Saliency detection via robust seed selection of foreground and background priors. In 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE. 2020. p. 797-801. (IEEE APSIPA ASC Proceedings Series). https://doi.org/10.1109/APSIPAASC47483.2019.9023263

Author

Jian, Muwei ; Wang, Ruihong ; Yu, Hui ; Dong, Junyu ; Wang, Yujuan ; Yin, Yilong ; Lam, Kin-Man. / Saliency detection via robust seed selection of foreground and background priors. 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2020. pp. 797-801 (IEEE APSIPA ASC Proceedings Series).

Bibtex

@inproceedings{5a0e509fcba84ca7bca58f0e909b7769,
title = "Saliency detection via robust seed selection of foreground and background priors",
abstract = "Recently, saliency detection has become a research hotspot in both the computer-vision and image-processing fields. Among the diverse saliency-detection approaches, those based on the foreground and background-based model can achieve promising performance. Reliable seed selection for the foreground and background priors is a critical step for successful saliency detection. In this paper, we firstly exploit the spatial distribution of the extracted directional patches to predict the centroid of each salient object in an image. Then, we adopt the located centroids as the visual-attention center of the whole image to compute the superpixel-based center prior, which can facilitate the subsequent seed selection for the foreground and background-prior generation. Finally, the two individual foreground-based and background-based saliency maps are combined together into a plausible and authentic saliency map. Extensive experimental assessments on publicly available datasets demonstrate the effectiveness of our proposed model.",
author = "Muwei Jian and Ruihong Wang and Hui Yu and Junyu Dong and Yujuan Wang and Yilong Yin and Kin-Man Lam",
year = "2020",
month = mar,
day = "5",
doi = "10.1109/APSIPAASC47483.2019.9023263",
language = "English",
isbn = "978-1-7281-3249-5",
series = "IEEE APSIPA ASC Proceedings Series",
publisher = "IEEE",
pages = "797--801",
booktitle = "2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)",
note = "2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2019 ; Conference date: 18-11-2019 Through 21-11-2019",
url = "http://apsipa2019.org/",

}

RIS

TY - GEN

T1 - Saliency detection via robust seed selection of foreground and background priors

AU - Jian, Muwei

AU - Wang, Ruihong

AU - Yu, Hui

AU - Dong, Junyu

AU - Wang, Yujuan

AU - Yin, Yilong

AU - Lam, Kin-Man

PY - 2020/3/5

Y1 - 2020/3/5

N2 - Recently, saliency detection has become a research hotspot in both the computer-vision and image-processing fields. Among the diverse saliency-detection approaches, those based on the foreground and background-based model can achieve promising performance. Reliable seed selection for the foreground and background priors is a critical step for successful saliency detection. In this paper, we firstly exploit the spatial distribution of the extracted directional patches to predict the centroid of each salient object in an image. Then, we adopt the located centroids as the visual-attention center of the whole image to compute the superpixel-based center prior, which can facilitate the subsequent seed selection for the foreground and background-prior generation. Finally, the two individual foreground-based and background-based saliency maps are combined together into a plausible and authentic saliency map. Extensive experimental assessments on publicly available datasets demonstrate the effectiveness of our proposed model.

AB - Recently, saliency detection has become a research hotspot in both the computer-vision and image-processing fields. Among the diverse saliency-detection approaches, those based on the foreground and background-based model can achieve promising performance. Reliable seed selection for the foreground and background priors is a critical step for successful saliency detection. In this paper, we firstly exploit the spatial distribution of the extracted directional patches to predict the centroid of each salient object in an image. Then, we adopt the located centroids as the visual-attention center of the whole image to compute the superpixel-based center prior, which can facilitate the subsequent seed selection for the foreground and background-prior generation. Finally, the two individual foreground-based and background-based saliency maps are combined together into a plausible and authentic saliency map. Extensive experimental assessments on publicly available datasets demonstrate the effectiveness of our proposed model.

U2 - 10.1109/APSIPAASC47483.2019.9023263

DO - 10.1109/APSIPAASC47483.2019.9023263

M3 - Conference contribution

SN - 978-1-7281-3249-5

T3 - IEEE APSIPA ASC Proceedings Series

SP - 797

EP - 801

BT - 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)

PB - IEEE

T2 - 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference

Y2 - 18 November 2019 through 21 November 2019

ER -

ID: 20528991