Learning the Spiral Sharing Network with Minimum Salient Region Regression for Saliency Detection

Zukai Chen; Xin Tan; Hengliang Zhu; Shouhong DIng; Lizhuang Ma; Haichuan Song

doi:10.1109/ICASSP.2019.8682531

Learning the Spiral Sharing Network with Minimum Salient Region Regression for Saliency Detection

Zukai Chen^*
, Xin Tan
, Hengliang Zhu
, Shouhong DIng
, Lizhuang Ma
, Haichuan Song

^*Corresponding author for this work

School of Computer Science and Technology

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

Abstract

With the development of convolutional neural networks (CNNs), saliency detection methods have made a big progress in recent years. However, the previous methods sometimes mistakenly highlight the non-salient region, especially in complex backgrounds. To solve this problem, a two-stage method for saliency detection is proposed in this paper. In the first stage, a network is used to regress the minimum salient region (RMSR) containing all salient objects. Then in the second stage, in order to fuse the multi-level features, the spiral sharing network (SSN) is proposed for pixel-level detection on the result of RMSR. Experimental results on four public datasets show that our model is effective over the state-of-the-art approaches.

Original language	English
Title of host publication	2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1667-1671
Number of pages	5
ISBN (Electronic)	9781479981311
DOIs	https://doi.org/10.1109/ICASSP.2019.8682531
State	Published - May 2019
Event	44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom Duration: 12 May 2019 → 17 May 2019

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	2019-May
ISSN (Print)	1520-6149

Conference

Conference	44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
Country/Territory	United Kingdom
City	Brighton
Period	12/05/19 → 17/05/19

Keywords

saliency detection
salient region
spiral sharing network

Access to Document

10.1109/ICASSP.2019.8682531

Cite this

Chen, Z., Tan, X., Zhu, H., DIng, S., Ma, L., & Song, H. (2019). Learning the Spiral Sharing Network with Minimum Salient Region Regression for Saliency Detection. In 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings (pp. 1667-1671). Article 8682531 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2019-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2019.8682531

Chen, Zukai ; Tan, Xin ; Zhu, Hengliang et al. / Learning the Spiral Sharing Network with Minimum Salient Region Regression for Saliency Detection. 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1667-1671 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

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title = "Learning the Spiral Sharing Network with Minimum Salient Region Regression for Saliency Detection",

abstract = "With the development of convolutional neural networks (CNNs), saliency detection methods have made a big progress in recent years. However, the previous methods sometimes mistakenly highlight the non-salient region, especially in complex backgrounds. To solve this problem, a two-stage method for saliency detection is proposed in this paper. In the first stage, a network is used to regress the minimum salient region (RMSR) containing all salient objects. Then in the second stage, in order to fuse the multi-level features, the spiral sharing network (SSN) is proposed for pixel-level detection on the result of RMSR. Experimental results on four public datasets show that our model is effective over the state-of-the-art approaches.",

keywords = "saliency detection, salient region, spiral sharing network",

author = "Zukai Chen and Xin Tan and Hengliang Zhu and Shouhong DIng and Lizhuang Ma and Haichuan Song",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 ; Conference date: 12-05-2019 Through 17-05-2019",

year = "2019",

month = may,

doi = "10.1109/ICASSP.2019.8682531",

language = "英语",

series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Chen, Z, Tan, X, Zhu, H, DIng, S, Ma, L & Song, H 2019, Learning the Spiral Sharing Network with Minimum Salient Region Regression for Saliency Detection. in 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings., 8682531, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2019-May, Institute of Electrical and Electronics Engineers Inc., pp. 1667-1671, 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019, Brighton, United Kingdom, 12/05/19. https://doi.org/10.1109/ICASSP.2019.8682531

Learning the Spiral Sharing Network with Minimum Salient Region Regression for Saliency Detection. / Chen, Zukai; Tan, Xin; Zhu, Hengliang et al.
2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1667-1671 8682531 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2019-May).

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

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AU - Chen, Zukai

AU - Tan, Xin

AU - Zhu, Hengliang

AU - DIng, Shouhong

AU - Ma, Lizhuang

AU - Song, Haichuan

PY - 2019/5

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AB - With the development of convolutional neural networks (CNNs), saliency detection methods have made a big progress in recent years. However, the previous methods sometimes mistakenly highlight the non-salient region, especially in complex backgrounds. To solve this problem, a two-stage method for saliency detection is proposed in this paper. In the first stage, a network is used to regress the minimum salient region (RMSR) containing all salient objects. Then in the second stage, in order to fuse the multi-level features, the spiral sharing network (SSN) is proposed for pixel-level detection on the result of RMSR. Experimental results on four public datasets show that our model is effective over the state-of-the-art approaches.

KW - saliency detection

KW - salient region

KW - spiral sharing network

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Chen Z, Tan X, Zhu H, DIng S, Ma L , Song H. Learning the Spiral Sharing Network with Minimum Salient Region Regression for Saliency Detection. In 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1667-1671. 8682531. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). doi: 10.1109/ICASSP.2019.8682531

Learning the Spiral Sharing Network with Minimum Salient Region Regression for Saliency Detection

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