Stereoscopic Image Saliency Detection Optimization: A Multi-Cue-Driven Approach
Numerous stereoscopic image saliency detection algorithms have been presented to detect the salient objects in a stereoscopic image. However, they typically fail to uniformly highlight all the objects when the image contains multiple objects or complex backgrounds. In this paper, we propose a multi-...
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doaj-8e561baf28124f209f33fed8ca7ccca02021-03-29T22:34:05ZengIEEEIEEE Access2169-35362019-01-017198351984710.1109/ACCESS.2019.28974048637929Stereoscopic Image Saliency Detection Optimization: A Multi-Cue-Driven ApproachYuzhen Niu0https://orcid.org/0000-0002-9874-9719Jianer Chen1Xiao Ke2Junhao Chen3Fujian Key Laboratory of Network Computing and Intelligent Information Processing, College of Mathematics and Computer Science, Fuzhou University, Fuzhou, ChinaFujian Key Laboratory of Network Computing and Intelligent Information Processing, College of Mathematics and Computer Science, Fuzhou University, Fuzhou, ChinaFujian Key Laboratory of Network Computing and Intelligent Information Processing, College of Mathematics and Computer Science, Fuzhou University, Fuzhou, ChinaFujian Key Laboratory of Network Computing and Intelligent Information Processing, College of Mathematics and Computer Science, Fuzhou University, Fuzhou, ChinaNumerous stereoscopic image saliency detection algorithms have been presented to detect the salient objects in a stereoscopic image. However, they typically fail to uniformly highlight all the objects when the image contains multiple objects or complex backgrounds. In this paper, we propose a multi-cue-driven optimization (MCDO) for stereoscopic image saliency detection. MCDO leverages multiple cues, including depth, color, and spatial position to optimize the saliency maps generated by existing saliency detection algorithms. Fully connected conditional random field is used to integrate the depth, color, and spatial cues from the input stereoscopic image to ensure that pixels with similar depth, color, and/or spatial position have similar saliency values. Compared with original saliency maps, the optimized saliency maps have more uniformly highlighted salient objects, whose boundaries are more precise, and fewer incorrectly detected background regions. The experimental results on three datasets demonstrate that the proposed MCDO method can effectively improve the performance of stereoscopic and 2-D image saliency detection algorithms.https://ieeexplore.ieee.org/document/8637929/Conditional random fielddepth informationmulti-cuesaliency detectionstereoscopic images |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yuzhen Niu Jianer Chen Xiao Ke Junhao Chen |
spellingShingle |
Yuzhen Niu Jianer Chen Xiao Ke Junhao Chen Stereoscopic Image Saliency Detection Optimization: A Multi-Cue-Driven Approach IEEE Access Conditional random field depth information multi-cue saliency detection stereoscopic images |
author_facet |
Yuzhen Niu Jianer Chen Xiao Ke Junhao Chen |
author_sort |
Yuzhen Niu |
title |
Stereoscopic Image Saliency Detection Optimization: A Multi-Cue-Driven Approach |
title_short |
Stereoscopic Image Saliency Detection Optimization: A Multi-Cue-Driven Approach |
title_full |
Stereoscopic Image Saliency Detection Optimization: A Multi-Cue-Driven Approach |
title_fullStr |
Stereoscopic Image Saliency Detection Optimization: A Multi-Cue-Driven Approach |
title_full_unstemmed |
Stereoscopic Image Saliency Detection Optimization: A Multi-Cue-Driven Approach |
title_sort |
stereoscopic image saliency detection optimization: a multi-cue-driven approach |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
Numerous stereoscopic image saliency detection algorithms have been presented to detect the salient objects in a stereoscopic image. However, they typically fail to uniformly highlight all the objects when the image contains multiple objects or complex backgrounds. In this paper, we propose a multi-cue-driven optimization (MCDO) for stereoscopic image saliency detection. MCDO leverages multiple cues, including depth, color, and spatial position to optimize the saliency maps generated by existing saliency detection algorithms. Fully connected conditional random field is used to integrate the depth, color, and spatial cues from the input stereoscopic image to ensure that pixels with similar depth, color, and/or spatial position have similar saliency values. Compared with original saliency maps, the optimized saliency maps have more uniformly highlighted salient objects, whose boundaries are more precise, and fewer incorrectly detected background regions. The experimental results on three datasets demonstrate that the proposed MCDO method can effectively improve the performance of stereoscopic and 2-D image saliency detection algorithms. |
topic |
Conditional random field depth information multi-cue saliency detection stereoscopic images |
url |
https://ieeexplore.ieee.org/document/8637929/ |
work_keys_str_mv |
AT yuzhenniu stereoscopicimagesaliencydetectionoptimizationamulticuedrivenapproach AT jianerchen stereoscopicimagesaliencydetectionoptimizationamulticuedrivenapproach AT xiaoke stereoscopicimagesaliencydetectionoptimizationamulticuedrivenapproach AT junhaochen stereoscopicimagesaliencydetectionoptimizationamulticuedrivenapproach |
_version_ |
1724191288161468416 |