article

Optimal contrast based saliency detection

Authors:

Xiaoliang Qian,

Lei GuoAuthors Info & Claims

Pattern Recognition Letters, Volume 34, Issue 11

Pages 1270 - 1278

https://doi.org/10.1016/j.patrec.2013.04.009

Published: 01 August 2013 Publication History

Abstract

Saliency detection has been gaining increasing attention in recent years since it could significantly boost many content-based multimedia applications. Most traditional approaches adopt the predefined local contrast, global contrast, or heuristic combination of them to measure saliency. In this paper, based on the underlying premises that human visual attention mechanisms work adaptively for various scales and salient objects can maximally pop out with respect to the background within a specific surrounding area, we propose a novel saliency detection method using a new concept of optimal contrast. A number of contrast hypotheses are first calculated with various surrounding areas by means of sparse coding principles. Afterwards, these hypotheses are compared using an entropy-based criterion and the optimal contrast is selected which is treated as the core factor for building the saliency map. Finally, a multi-scale enhancement is performed to further refine the results. Comprehensive evaluations on three publicly available benchmark datasets and comparisons with many up-to-date algorithms demonstrate the effectiveness of the proposed work.

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Cited By

Han JCheng GLi ZZhang D(2018)A Unified Metric Learning-Based Framework for Co-Saliency DetectionIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2017.270626428:10(2473-2483)Online publication date: 24-Oct-2018
https://dl.acm.org/doi/10.1109/TCSVT.2017.2706264
Gao FYou JWang JSun JYang EZhou H(2017)A novel target detection method for SAR images based on shadow proposal and saliency analysisNeurocomputing10.1016/j.neucom.2017.06.004267:C(220-231)Online publication date: 6-Dec-2017
https://dl.acm.org/doi/10.1016/j.neucom.2017.06.004
Filali IAllili MBenblidia N(2016)Multi-scale salient object detection using graph ranking and global-local saliency refinementImage Communication10.1016/j.image.2016.07.00747:C(380-401)Online publication date: 1-Sep-2016
https://dl.acm.org/doi/10.1016/j.image.2016.07.007
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Published In

cover image Pattern Recognition Letters

Pattern Recognition Letters Volume 34, Issue 11

August, 2013

109 pages

ISSN:0167-8655

Issue’s Table of Contents

Copyright © Elsevier B.V. © 2013.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 August 2013

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Cited By

Han JCheng GLi ZZhang D(2018)A Unified Metric Learning-Based Framework for Co-Saliency DetectionIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2017.270626428:10(2473-2483)Online publication date: 24-Oct-2018
https://dl.acm.org/doi/10.1109/TCSVT.2017.2706264
Gao FYou JWang JSun JYang EZhou H(2017)A novel target detection method for SAR images based on shadow proposal and saliency analysisNeurocomputing10.1016/j.neucom.2017.06.004267:C(220-231)Online publication date: 6-Dec-2017
https://dl.acm.org/doi/10.1016/j.neucom.2017.06.004
Filali IAllili MBenblidia N(2016)Multi-scale salient object detection using graph ranking and global-local saliency refinementImage Communication10.1016/j.image.2016.07.00747:C(380-401)Online publication date: 1-Sep-2016
https://dl.acm.org/doi/10.1016/j.image.2016.07.007
Junwei Han Dingwen Zhang Xintao Hu Lei Guo Jinchang Ren Feng Wu (2015)Background Prior-Based Salient Object Detection via Deep Reconstruction ResidualIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2014.238147125:8(1309-1321)Online publication date: 1-Aug-2015
https://dl.acm.org/doi/10.1109/TCSVT.2014.2381471
Fareed MAhmed GChun Q(2015)Salient region detection through sparse reconstruction and graph-based rankingJournal of Visual Communication and Image Representation10.1016/j.jvcir.2015.08.00232:C(144-155)Online publication date: 1-Oct-2015
https://dl.acm.org/doi/10.1016/j.jvcir.2015.08.002
Xu XMu NZhang HFu X(undefined)Salient object detection from distinctive features in low contrast images2015 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP.2015.7351379(3126-3130)
https://dl.acm.org/doi/10.1109/ICIP.2015.7351379

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