Real-time salient object detection based on fully convolutional networks

Guangyu Nie; Yinan Guo; Yue Liu; Yongtian Wang

doi:10.1007/978-981-10-7389-2_19

Real-time salient object detection based on fully convolutional networks

Guangyu Nie
, Yinan Guo
, Yue Liu^*
, Yongtian Wang

^*此作品的通讯作者

光电学院

Beijing Institute of Technology
China Mobile Communications Group Co., Ltd.

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

12 引用（Scopus）

摘要

Salient object detection allows to take into account the visual content of images. In this paper, we train a real-time saliency model based on fully convolutional network (FCN), and then combine the energy maps with Gaussian filters to generate a multi-resolution image. The proposed method has been tested both qualitatively and quantitatively, by considering a representative set of ground truth images labeled with corresponding salient objects. Experimental results demonstrate that the proposed deep model significantly is superior to the-state-of-the-art approaches.

源语言	英语
主期刊名	Advances in Image and Graphics Technologies - 12th Chinese conference, IGTA 2017, Revised Selected Papers
编辑	Xiaoru Yuan, Henry Been-Lirn Duh, Yongtian Wang, Yue Liu, Jian Yang, Shengjin Wang, Ran He
出版商	Springer Verlag
页	189-198
页数	10
ISBN（印刷版）	9789811073885
DOI	https://doi.org/10.1007/978-981-10-7389-2_19
出版状态	已出版 - 2018
活动	12th Chinese conference on Advances in Image and Graphics Technologies, IGTA 2017 - Beijing, 中国期限: 30 6月 2017 → 1 7月 2017

出版系列

姓名	Communications in Computer and Information Science
卷	757
ISSN（印刷版）	1865-0929

会议

会议	12th Chinese conference on Advances in Image and Graphics Technologies, IGTA 2017
国家/地区	中国
市	Beijing
时期	30/06/17 → 1/07/17

访问文件

10.1007/978-981-10-7389-2_19

其它文件与链接

链接到 Scopus 的出版物

引用此

Nie, G., Guo, Y., Liu, Y., & Wang, Y. (2018). Real-time salient object detection based on fully convolutional networks. 在 X. Yuan, H. B.-L. Duh, Y. Wang, Y. Liu, J. Yang, S. Wang, & R. He (编辑), Advances in Image and Graphics Technologies - 12th Chinese conference, IGTA 2017, Revised Selected Papers (页码 189-198). (Communications in Computer and Information Science; 卷 757). Springer Verlag. https://doi.org/10.1007/978-981-10-7389-2_19

Nie, Guangyu ; Guo, Yinan ; Liu, Yue 等. / Real-time salient object detection based on fully convolutional networks. Advances in Image and Graphics Technologies - 12th Chinese conference, IGTA 2017, Revised Selected Papers. 编辑 / Xiaoru Yuan ; Henry Been-Lirn Duh ; Yongtian Wang ; Yue Liu ; Jian Yang ; Shengjin Wang ; Ran He. Springer Verlag, 2018. 页码 189-198 (Communications in Computer and Information Science).

@inproceedings{5e5340ea55bf429085d731d9f81cb937,

title = "Real-time salient object detection based on fully convolutional networks",

abstract = "Salient object detection allows to take into account the visual content of images. In this paper, we train a real-time saliency model based on fully convolutional network (FCN), and then combine the energy maps with Gaussian filters to generate a multi-resolution image. The proposed method has been tested both qualitatively and quantitatively, by considering a representative set of ground truth images labeled with corresponding salient objects. Experimental results demonstrate that the proposed deep model significantly is superior to the-state-of-the-art approaches.",

keywords = "FCN, Real-time, Saliency detection",

author = "Guangyu Nie and Yinan Guo and Yue Liu and Yongtian Wang",

note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2018.; 12th Chinese conference on Advances in Image and Graphics Technologies, IGTA 2017 ; Conference date: 30-06-2017 Through 01-07-2017",

year = "2018",

doi = "10.1007/978-981-10-7389-2\_19",

language = "English",

isbn = "9789811073885",

series = "Communications in Computer and Information Science",

publisher = "Springer Verlag",

pages = "189--198",

editor = "Xiaoru Yuan and Duh, \{Henry Been-Lirn\} and Yongtian Wang and Yue Liu and Jian Yang and Shengjin Wang and Ran He",

booktitle = "Advances in Image and Graphics Technologies - 12th Chinese conference, IGTA 2017, Revised Selected Papers",

address = "Germany",

}

Nie, G, Guo, Y, Liu, Y & Wang, Y 2018, Real-time salient object detection based on fully convolutional networks. 在 X Yuan, HB-L Duh, Y Wang, Y Liu, J Yang, S Wang & R He (编辑), Advances in Image and Graphics Technologies - 12th Chinese conference, IGTA 2017, Revised Selected Papers. Communications in Computer and Information Science, 卷 757, Springer Verlag, 页码 189-198, 12th Chinese conference on Advances in Image and Graphics Technologies, IGTA 2017, Beijing, 中国, 30/06/17. https://doi.org/10.1007/978-981-10-7389-2_19

Real-time salient object detection based on fully convolutional networks. / Nie, Guangyu; Guo, Yinan; Liu, Yue 等.
Advances in Image and Graphics Technologies - 12th Chinese conference, IGTA 2017, Revised Selected Papers. 编辑 / Xiaoru Yuan; Henry Been-Lirn Duh; Yongtian Wang; Yue Liu; Jian Yang; Shengjin Wang; Ran He. Springer Verlag, 2018. 页码 189-198 (Communications in Computer and Information Science; 卷 757).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - Real-time salient object detection based on fully convolutional networks

AU - Nie, Guangyu

AU - Guo, Yinan

AU - Liu, Yue

AU - Wang, Yongtian

N1 - Publisher Copyright: © Springer Nature Singapore Pte Ltd. 2018.

PY - 2018

Y1 - 2018

N2 - Salient object detection allows to take into account the visual content of images. In this paper, we train a real-time saliency model based on fully convolutional network (FCN), and then combine the energy maps with Gaussian filters to generate a multi-resolution image. The proposed method has been tested both qualitatively and quantitatively, by considering a representative set of ground truth images labeled with corresponding salient objects. Experimental results demonstrate that the proposed deep model significantly is superior to the-state-of-the-art approaches.

AB - Salient object detection allows to take into account the visual content of images. In this paper, we train a real-time saliency model based on fully convolutional network (FCN), and then combine the energy maps with Gaussian filters to generate a multi-resolution image. The proposed method has been tested both qualitatively and quantitatively, by considering a representative set of ground truth images labeled with corresponding salient objects. Experimental results demonstrate that the proposed deep model significantly is superior to the-state-of-the-art approaches.

KW - FCN

KW - Real-time

KW - Saliency detection

UR - https://www.scopus.com/pages/publications/85036451646

U2 - 10.1007/978-981-10-7389-2_19

DO - 10.1007/978-981-10-7389-2_19

M3 - Conference contribution

AN - SCOPUS:85036451646

SN - 9789811073885

T3 - Communications in Computer and Information Science

SP - 189

EP - 198

BT - Advances in Image and Graphics Technologies - 12th Chinese conference, IGTA 2017, Revised Selected Papers

A2 - Yuan, Xiaoru

A2 - Duh, Henry Been-Lirn

A2 - Wang, Yongtian

A2 - Liu, Yue

A2 - Yang, Jian

A2 - Wang, Shengjin

A2 - He, Ran

PB - Springer Verlag

T2 - 12th Chinese conference on Advances in Image and Graphics Technologies, IGTA 2017

Y2 - 30 June 2017 through 1 July 2017

ER -

Nie G, Guo Y, Liu Y , Wang Y. Real-time salient object detection based on fully convolutional networks. 在 Yuan X, Duh HBL, Wang Y, Liu Y, Yang J, Wang S, He R, 编辑, Advances in Image and Graphics Technologies - 12th Chinese conference, IGTA 2017, Revised Selected Papers. Springer Verlag. 2018. 页码 189-198. (Communications in Computer and Information Science). doi: 10.1007/978-981-10-7389-2_19

Real-time salient object detection based on fully convolutional networks

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