Visual saliency detection via background features and object-location cues

Muwei Jian; Jing Wang; Hui Yu; Yakun Ju

doi:10.23919/IConAC.2019.8895226

Visual saliency detection via background features and object-location cues

Muwei Jian, Jing Wang, Hui Yu, Yakun Ju

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

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Abstract

In this paper, we propose a simple visual saliencydetection model based on spatial position of salient objects and background cues. At first, discrete wavelet frame transform (DWDT) are used to extract directionality characteristics for estimating the centoid of salient objects in the input image. Then, the colour contrast feature performed is to represent the physical characteristics of salient objects. Conversely, sparse dictionary learning is applied to obtain the background feature map. Finally, three typical cues of the directional feature, the colour contrast feature and the background feature are mixed to generate a credible saliency map. Experimental results verify that the designed method is useful and effective.

Original language	English
Title of host publication	Proceedings of the 2019 25th International Conference on Automation and Computing (ICAC)
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-4
Number of pages	4
ISBN (Electronic)	978-1-8613-7665-7
ISBN (Print)	978-1-7281-2518-3
DOIs	https://doi.org/10.23919/IConAC.2019.8895226
Publication status	Published - 11 Nov 2019
Event	25th IEEE International Conference on Automation and Computing - Lancaster, United Kingdom Duration: 5 Sept 2019 → 7 Sept 2019

Conference

Conference	25th IEEE International Conference on Automation and Computing
Abbreviated title	ICAC'19
Country/Territory	United Kingdom
City	Lancaster
Period	5/09/19 → 7/09/19

Keywords

discrete wavelet transform
saliency detection
background features
position prior

Access to Document

10.23919/IConAC.2019.8895226

ICAC - postprint
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Accepted author manuscript (Post-print), 1.01 MB

http://www.cacsuk.co.uk/index.php/conferences/icac

Cite this

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title = "Visual saliency detection via background features and object-location cues",

abstract = "In this paper, we propose a simple visual saliencydetection model based on spatial position of salient objects and background cues. At first, discrete wavelet frame transform (DWDT) are used to extract directionality characteristics for estimating the centoid of salient objects in the input image. Then, the colour contrast feature performed is to represent the physical characteristics of salient objects. Conversely, sparse dictionary learning is applied to obtain the background feature map. Finally, three typical cues of the directional feature, the colour contrast feature and the background feature are mixed to generate a credible saliency map. Experimental results verify that the designed method is useful and effective.",

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author = "Muwei Jian and Jing Wang and Hui Yu and Yakun Ju",

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year = "2019",

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doi = "10.23919/IConAC.2019.8895226",

language = "English",

isbn = "978-1-7281-2518-3",

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booktitle = "Proceedings of the 2019 25th International Conference on Automation and Computing (ICAC)",

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

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Jian, M, Wang, J, Yu, H & Ju, Y 2019, Visual saliency detection via background features and object-location cues. in Proceedings of the 2019 25th International Conference on Automation and Computing (ICAC). Institute of Electrical and Electronics Engineers Inc., pp. 1-4, 25th IEEE International Conference on Automation and Computing, Lancaster, United Kingdom, 5/09/19. https://doi.org/10.23919/IConAC.2019.8895226

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AU - Wang, Jing

AU - Yu, Hui

AU - Ju, Yakun

N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.

PY - 2019/11/11

Y1 - 2019/11/11

N2 - In this paper, we propose a simple visual saliencydetection model based on spatial position of salient objects and background cues. At first, discrete wavelet frame transform (DWDT) are used to extract directionality characteristics for estimating the centoid of salient objects in the input image. Then, the colour contrast feature performed is to represent the physical characteristics of salient objects. Conversely, sparse dictionary learning is applied to obtain the background feature map. Finally, three typical cues of the directional feature, the colour contrast feature and the background feature are mixed to generate a credible saliency map. Experimental results verify that the designed method is useful and effective.

AB - In this paper, we propose a simple visual saliencydetection model based on spatial position of salient objects and background cues. At first, discrete wavelet frame transform (DWDT) are used to extract directionality characteristics for estimating the centoid of salient objects in the input image. Then, the colour contrast feature performed is to represent the physical characteristics of salient objects. Conversely, sparse dictionary learning is applied to obtain the background feature map. Finally, three typical cues of the directional feature, the colour contrast feature and the background feature are mixed to generate a credible saliency map. Experimental results verify that the designed method is useful and effective.

KW - discrete wavelet transform

KW - saliency detection

KW - background features

KW - position prior

UR - http://www.cacsuk.co.uk/index.php/search/icac-ijac

UR - https://ieeexplore.ieee.org/xpl/conhome/1800563/all-proceedings

U2 - 10.23919/IConAC.2019.8895226

DO - 10.23919/IConAC.2019.8895226

M3 - Conference contribution

SN - 978-1-7281-2518-3

SP - 1

EP - 4

BT - Proceedings of the 2019 25th International Conference on Automation and Computing (ICAC)

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 25th IEEE International Conference on Automation and Computing

Y2 - 5 September 2019 through 7 September 2019

ER -

Visual saliency detection via background features and object-location cues

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