Salient Object Detection Integrating Both Background and Foreground Information Based on Manifold Preserving

Graph-based two-stage algorithms have widely developed and achieved good performance to detect salient objects. For these algorithms, choosing the proper seeds using for saliency propagation is quite crucial and difficult. In this paper, we consider using background/foreground probability values of...
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Autor*in:	Baoyan Wang [verfasserIn] Tie Zhang [verfasserIn] Xingang Wang [verfasserIn] Haijuan Hu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Salient object detection saliency map background foreground manifold

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 7(2019), Seite 126831-126841
Übergeordnetes Werk:	volume:7 ; year:2019 ; pages:126831-126841

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2019.2936915

Katalog-ID:	DOAJ068514190

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allfieldsSound	10.1109/ACCESS.2019.2936915 doi (DE-627)DOAJ068514190 (DE-599)DOAJ50c98d68240044748443df5e004c0627 DE-627 ger DE-627 rakwb eng TK1-9971 Baoyan Wang verfasserin aut Salient Object Detection Integrating Both Background and Foreground Information Based on Manifold Preserving 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Graph-based two-stage algorithms have widely developed and achieved good performance to detect salient objects. For these algorithms, choosing the proper seeds using for saliency propagation is quite crucial and difficult. In this paper, we consider using background/foreground probability values of candidate background/foreground seeds as the estimation of the reliable seeds, not considering the refinement of candidate seeds. Moreover, these probability values are integrated into the proposed saliency models, which can avoid hard filtering for candidate seeds as well as simplify the procedure of the algorithm. In addition, considering the manifold structure of an image, we fuse the manifold-preserving term into the saliency models. Especially, reconstruction matrix $A$ is determined based on the deep features extracted from FCN-32s, which can further improve detection performance of salient objects. The results of experiments in which the proposed SBFMP algorithm is applied to four datasets demonstrate SBFMP algorithm is prior to some existing state-of-the-art algorithms in terms of the different metrics. Salient object detection saliency map background foreground manifold Electrical engineering. Electronics. Nuclear engineering Tie Zhang verfasserin aut Xingang Wang verfasserin aut Haijuan Hu verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 126831-126841 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:126831-126841 https://doi.org/10.1109/ACCESS.2019.2936915 kostenfrei https://doaj.org/article/50c98d68240044748443df5e004c0627 kostenfrei https://ieeexplore.ieee.org/document/8809712/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 126831-126841
language	English
source	In IEEE Access 7(2019), Seite 126831-126841 volume:7 year:2019 pages:126831-126841
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topic_facet	Salient object detection saliency map background foreground manifold Electrical engineering. Electronics. Nuclear engineering
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container_title	IEEE Access
authorswithroles_txt_mv	Baoyan Wang @@aut@@ Tie Zhang @@aut@@ Xingang Wang @@aut@@ Haijuan Hu @@aut@@
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callnumber-first	T - Technology
author	Baoyan Wang
spellingShingle	Baoyan Wang misc TK1-9971 misc Salient object detection misc saliency map misc background misc foreground misc manifold misc Electrical engineering. Electronics. Nuclear engineering Salient Object Detection Integrating Both Background and Foreground Information Based on Manifold Preserving
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topic_title	TK1-9971 Salient Object Detection Integrating Both Background and Foreground Information Based on Manifold Preserving Salient object detection saliency map background foreground manifold
topic	misc TK1-9971 misc Salient object detection misc saliency map misc background misc foreground misc manifold misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Salient object detection misc saliency map misc background misc foreground misc manifold misc Electrical engineering. Electronics. Nuclear engineering
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title	Salient Object Detection Integrating Both Background and Foreground Information Based on Manifold Preserving
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title_full	Salient Object Detection Integrating Both Background and Foreground Information Based on Manifold Preserving
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title_sort	salient object detection integrating both background and foreground information based on manifold preserving
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title_auth	Salient Object Detection Integrating Both Background and Foreground Information Based on Manifold Preserving
abstract	Graph-based two-stage algorithms have widely developed and achieved good performance to detect salient objects. For these algorithms, choosing the proper seeds using for saliency propagation is quite crucial and difficult. In this paper, we consider using background/foreground probability values of candidate background/foreground seeds as the estimation of the reliable seeds, not considering the refinement of candidate seeds. Moreover, these probability values are integrated into the proposed saliency models, which can avoid hard filtering for candidate seeds as well as simplify the procedure of the algorithm. In addition, considering the manifold structure of an image, we fuse the manifold-preserving term into the saliency models. Especially, reconstruction matrix $A$ is determined based on the deep features extracted from FCN-32s, which can further improve detection performance of salient objects. The results of experiments in which the proposed SBFMP algorithm is applied to four datasets demonstrate SBFMP algorithm is prior to some existing state-of-the-art algorithms in terms of the different metrics.
abstractGer	Graph-based two-stage algorithms have widely developed and achieved good performance to detect salient objects. For these algorithms, choosing the proper seeds using for saliency propagation is quite crucial and difficult. In this paper, we consider using background/foreground probability values of candidate background/foreground seeds as the estimation of the reliable seeds, not considering the refinement of candidate seeds. Moreover, these probability values are integrated into the proposed saliency models, which can avoid hard filtering for candidate seeds as well as simplify the procedure of the algorithm. In addition, considering the manifold structure of an image, we fuse the manifold-preserving term into the saliency models. Especially, reconstruction matrix $A$ is determined based on the deep features extracted from FCN-32s, which can further improve detection performance of salient objects. The results of experiments in which the proposed SBFMP algorithm is applied to four datasets demonstrate SBFMP algorithm is prior to some existing state-of-the-art algorithms in terms of the different metrics.
abstract_unstemmed	Graph-based two-stage algorithms have widely developed and achieved good performance to detect salient objects. For these algorithms, choosing the proper seeds using for saliency propagation is quite crucial and difficult. In this paper, we consider using background/foreground probability values of candidate background/foreground seeds as the estimation of the reliable seeds, not considering the refinement of candidate seeds. Moreover, these probability values are integrated into the proposed saliency models, which can avoid hard filtering for candidate seeds as well as simplify the procedure of the algorithm. In addition, considering the manifold structure of an image, we fuse the manifold-preserving term into the saliency models. Especially, reconstruction matrix $A$ is determined based on the deep features extracted from FCN-32s, which can further improve detection performance of salient objects. The results of experiments in which the proposed SBFMP algorithm is applied to four datasets demonstrate SBFMP algorithm is prior to some existing state-of-the-art algorithms in terms of the different metrics.
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title_short	Salient Object Detection Integrating Both Background and Foreground Information Based on Manifold Preserving
url	https://doi.org/10.1109/ACCESS.2019.2936915 https://doaj.org/article/50c98d68240044748443df5e004c0627 https://ieeexplore.ieee.org/document/8809712/ https://doaj.org/toc/2169-3536
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Salient Object Detection Integrating Both Background and Foreground Information Based on Manifold Preserving

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