Saliency Detection via Depth-Induced Cellular Automata on Light Field
Piao, Yongri1; Li, Xiao1; Zhang, Miao2,3; Yu, Jingyi4; Lu, Huchuan1
2020
Source PublicationIEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN1057-7149
EISSN1941-0042
Volume29Pages:1879-1889
Status已发表
DOI10.1109/TIP.2019.2942434
AbstractIncorrect saliency detection such as false alarms and missed alarms may lead to potentially severe consequences in various application areas. Effective separation of salient objects in complex scenes is a major challenge in saliency detection. In this paper, we propose a new method for saliency detection on light field to improve the saliency detection in challenging scenes. We construct an object-guided depth map, which acts as an inducer to efficiently incorporate the relations among light field cues, by using abundant light field cues. Furthermore, we enforce spatial consistency by constructing an optimization model, named Depth-induced Cellular Automata (DCA), in which the saliency value of each superpixel is updated by exploiting the intrinsic relevance of its similar regions. Additionally, the proposed DCA model enables inaccurate saliency maps to achieve a high level of accuracy. We analyze our approach on one publicly available dataset. Experiments show the proposed method is robust to a wide range of challenging scenes and outperforms the state-of-the-art 2D/3D/4D (light-field) saliency detection approaches.
KeywordSaliency detection Image color analysis Automata Three-dimensional displays Two dimensional displays Visualization Computational modeling Saliency detection light field focusness cue depth cue depth-induced cellular automata (DCA) model
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Indexed BySCI ; EI
Language英语
Funding ProjectFundamental Research Funds for the Central Universities[DUT19JC58]
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000501324900017
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS KeywordVISUAL SALIENCY
Original Document TypeArticle
Citation statistics
Cited Times:6[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttps://kms.shanghaitech.edu.cn/handle/2MSLDSTB/104516
Collection信息科学与技术学院_PI研究组_虞晶怡组
Corresponding AuthorZhang, Miao
Affiliation1.Dalian Univ Technol, Sch Informat & Commun Engn, Dalian 116024, Peoples R China
2.Dalian Univ Technol, DUT RU Int Sch Informat Sci & Engn, Dalian 116024, Peoples R China
3.Key Lab Ubiquitous Network & Serv Software Liaoni, Dalian 116024, Peoples R China
4.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China
Recommended Citation
GB/T 7714
Piao, Yongri,Li, Xiao,Zhang, Miao,et al. Saliency Detection via Depth-Induced Cellular Automata on Light Field[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2020,29:1879-1889.
APA Piao, Yongri,Li, Xiao,Zhang, Miao,Yu, Jingyi,&Lu, Huchuan.(2020).Saliency Detection via Depth-Induced Cellular Automata on Light Field.IEEE TRANSACTIONS ON IMAGE PROCESSING,29,1879-1889.
MLA Piao, Yongri,et al."Saliency Detection via Depth-Induced Cellular Automata on Light Field".IEEE TRANSACTIONS ON IMAGE PROCESSING 29(2020):1879-1889.
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