Object-level salience detection by progressively enhanced network

Wang Yuan; Haichuan Song; Xin Tan; Chengwei Chen; Shouhong Ding; Lizhuang Ma

doi:10.1007/978-3-030-30508-6_30

Object-level salience detection by progressively enhanced network

Wang Yuan^*
, Haichuan Song
, Xin Tan
, Chengwei Chen
, Shouhong Ding
, Lizhuang Ma

^*Corresponding author for this work

School of Computer Science and Technology

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

Abstract

Saliency detection plays an important role in computer vision area. However, most of the previous works focus on detecting the salient regions, rather than the objects, which is more reasonable in many practical applications. In this paper, a framework is proposed for detecting the salient objects in input images. This framework is composed of two main components: (1) progressively enhanced network (PEN) for amplifying the specified layers of the network and merging the global context simultaneously; (2) object-level boundary extraction module (OBEM) for extracting the complete boundary of the salient object. Experiments and comparisons show that the proposed framework achieves state-of-the-art results. Especially on many challenging datasets, our method performs much better than other methods.

Original language	English
Title of host publication	Artificial Neural Networks and Machine Learning – ICANN 2019
Subtitle of host publication	Image Processing - 28th International Conference on Artificial Neural Networks, 2019, Proceedings
Editors	Igor V. Tetko, Pavel Karpov, Fabian Theis, Vera Kurková
Publisher	Springer Verlag
Pages	371-382
Number of pages	12
ISBN (Print)	9783030305079
DOIs	https://doi.org/10.1007/978-3-030-30508-6_30
State	Published - 2019
Event	28th International Conference on Artificial Neural Networks, ICANN 2019 - Munich, Germany Duration: 17 Sep 2019 → 19 Sep 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11729 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	28th International Conference on Artificial Neural Networks, ICANN 2019
Country/Territory	Germany
City	Munich
Period	17/09/19 → 19/09/19

Keywords

Global context
Object-level boundary extract
Progressively enhanced network
Saliency detection

Access to Document

10.1007/978-3-030-30508-6_30

Cite this

Yuan, W., Song, H., Tan, X., Chen, C., Ding, S., & Ma, L. (2019). Object-level salience detection by progressively enhanced network. In I. V. Tetko, P. Karpov, F. Theis, & V. Kurková (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing - 28th International Conference on Artificial Neural Networks, 2019, Proceedings (pp. 371-382). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11729 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-30508-6_30

Yuan, Wang ; Song, Haichuan ; Tan, Xin et al. / Object-level salience detection by progressively enhanced network. Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing - 28th International Conference on Artificial Neural Networks, 2019, Proceedings. editor / Igor V. Tetko ; Pavel Karpov ; Fabian Theis ; Vera Kurková. Springer Verlag, 2019. pp. 371-382 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Object-level salience detection by progressively enhanced network",

abstract = "Saliency detection plays an important role in computer vision area. However, most of the previous works focus on detecting the salient regions, rather than the objects, which is more reasonable in many practical applications. In this paper, a framework is proposed for detecting the salient objects in input images. This framework is composed of two main components: (1) progressively enhanced network (PEN) for amplifying the specified layers of the network and merging the global context simultaneously; (2) object-level boundary extraction module (OBEM) for extracting the complete boundary of the salient object. Experiments and comparisons show that the proposed framework achieves state-of-the-art results. Especially on many challenging datasets, our method performs much better than other methods.",

keywords = "Global context, Object-level boundary extract, Progressively enhanced network, Saliency detection",

author = "Wang Yuan and Haichuan Song and Xin Tan and Chengwei Chen and Shouhong Ding and Lizhuang Ma",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 28th International Conference on Artificial Neural Networks, ICANN 2019 ; Conference date: 17-09-2019 Through 19-09-2019",

year = "2019",

doi = "10.1007/978-3-030-30508-6\_30",

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Yuan, W, Song, H, Tan, X, Chen, C, Ding, S & Ma, L 2019, Object-level salience detection by progressively enhanced network. in IV Tetko, P Karpov, F Theis & V Kurková (eds), Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing - 28th International Conference on Artificial Neural Networks, 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11729 LNCS, Springer Verlag, pp. 371-382, 28th International Conference on Artificial Neural Networks, ICANN 2019, Munich, Germany, 17/09/19. https://doi.org/10.1007/978-3-030-30508-6_30

Object-level salience detection by progressively enhanced network. / Yuan, Wang; Song, Haichuan; Tan, Xin et al.
Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing - 28th International Conference on Artificial Neural Networks, 2019, Proceedings. ed. / Igor V. Tetko; Pavel Karpov; Fabian Theis; Vera Kurková. Springer Verlag, 2019. p. 371-382 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11729 LNCS).

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

TY - GEN

T1 - Object-level salience detection by progressively enhanced network

AU - Yuan, Wang

AU - Song, Haichuan

AU - Tan, Xin

AU - Chen, Chengwei

AU - Ding, Shouhong

AU - Ma, Lizhuang

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2019.

PY - 2019

Y1 - 2019

N2 - Saliency detection plays an important role in computer vision area. However, most of the previous works focus on detecting the salient regions, rather than the objects, which is more reasonable in many practical applications. In this paper, a framework is proposed for detecting the salient objects in input images. This framework is composed of two main components: (1) progressively enhanced network (PEN) for amplifying the specified layers of the network and merging the global context simultaneously; (2) object-level boundary extraction module (OBEM) for extracting the complete boundary of the salient object. Experiments and comparisons show that the proposed framework achieves state-of-the-art results. Especially on many challenging datasets, our method performs much better than other methods.

AB - Saliency detection plays an important role in computer vision area. However, most of the previous works focus on detecting the salient regions, rather than the objects, which is more reasonable in many practical applications. In this paper, a framework is proposed for detecting the salient objects in input images. This framework is composed of two main components: (1) progressively enhanced network (PEN) for amplifying the specified layers of the network and merging the global context simultaneously; (2) object-level boundary extraction module (OBEM) for extracting the complete boundary of the salient object. Experiments and comparisons show that the proposed framework achieves state-of-the-art results. Especially on many challenging datasets, our method performs much better than other methods.

KW - Global context

KW - Object-level boundary extract

KW - Progressively enhanced network

KW - Saliency detection

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DO - 10.1007/978-3-030-30508-6_30

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AN - SCOPUS:85072866838

SN - 9783030305079

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 371

EP - 382

BT - Artificial Neural Networks and Machine Learning – ICANN 2019

A2 - Tetko, Igor V.

A2 - Karpov, Pavel

A2 - Theis, Fabian

A2 - Kurková, Vera

PB - Springer Verlag

T2 - 28th International Conference on Artificial Neural Networks, ICANN 2019

Y2 - 17 September 2019 through 19 September 2019

ER -

Yuan W, Song H, Tan X, Chen C, Ding S, Ma L. Object-level salience detection by progressively enhanced network. In Tetko IV, Karpov P, Theis F, Kurková V, editors, Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing - 28th International Conference on Artificial Neural Networks, 2019, Proceedings. Springer Verlag. 2019. p. 371-382. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-30508-6_30

Object-level salience detection by progressively enhanced network

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