Review of Visual Saliency Detection With Comprehensive Information
The visual saliency detection model simulates the human visual system to perceive the scene and has been widely used in many vision tasks. With the development of acquisition technology, more comprehensive information, such as depth cue, inter-image correspondence, or temporal relationship, is avail...
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| Published in | IEEE transactions on circuits and systems for video technology Vol. 29; no. 10; pp. 2941 - 2959 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
IEEE
01.10.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
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| Abstract | The visual saliency detection model simulates the human visual system to perceive the scene and has been widely used in many vision tasks. With the development of acquisition technology, more comprehensive information, such as depth cue, inter-image correspondence, or temporal relationship, is available to extend image saliency detection to RGBD saliency detection, co-saliency detection, or video saliency detection. The RGBD saliency detection model focuses on extracting the salient regions from RGBD images by combining the depth information. The co-saliency detection model introduces the inter-image correspondence constraint to discover the common salient object in an image group. The goal of the video saliency detection model is to locate the motion-related salient object in video sequences, which considers the motion cue and spatiotemporal constraint jointly. In this paper, we review different types of saliency detection algorithms, summarize the important issues of the existing methods, and discuss the existent problems and future works. Moreover, the evaluation datasets and quantitative measurements are briefly introduced, and the experimental analysis and discussion are conducted to provide a holistic overview of different saliency detection methods. |
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| AbstractList | The visual saliency detection model simulates the human visual system to perceive the scene and has been widely used in many vision tasks. With the development of acquisition technology, more comprehensive information, such as depth cue, inter-image correspondence, or temporal relationship, is available to extend image saliency detection to RGBD saliency detection, co-saliency detection, or video saliency detection. The RGBD saliency detection model focuses on extracting the salient regions from RGBD images by combining the depth information. The co-saliency detection model introduces the inter-image correspondence constraint to discover the common salient object in an image group. The goal of the video saliency detection model is to locate the motion-related salient object in video sequences, which considers the motion cue and spatiotemporal constraint jointly. In this paper, we review different types of saliency detection algorithms, summarize the important issues of the existing methods, and discuss the existent problems and future works. Moreover, the evaluation datasets and quantitative measurements are briefly introduced, and the experimental analysis and discussion are conducted to provide a holistic overview of different saliency detection methods. |
| Author | Huang, Qingming Cong, Runmin Lin, Weisi Fu, Huazhu Cheng, Ming-Ming Lei, Jianjun |
| Author_xml | – sequence: 1 givenname: Runmin orcidid: 0000-0003-0972-4008 surname: Cong fullname: Cong, Runmin email: rmcong@tju.edu.cn organization: School of Electrical and Information Engineering, Tianjin University, Tianjin, China – sequence: 2 givenname: Jianjun orcidid: 0000-0003-3171-7680 surname: Lei fullname: Lei, Jianjun email: jjlei@tju.edu.cn organization: School of Electrical and Information Engineering, Tianjin University, Tianjin, China – sequence: 3 givenname: Huazhu orcidid: 0000-0002-9702-5524 surname: Fu fullname: Fu, Huazhu email: huazhufu@gmail.com organization: Inception Institute of Artificial Intelligence, Abu Dhabi, United Arab Emirates – sequence: 4 givenname: Ming-Ming orcidid: 0000-0001-5550-8758 surname: Cheng fullname: Cheng, Ming-Ming email: cmm@nankai.edu.cn organization: School of Computer and Control Engineering, Nankai University, Tianjin, China – sequence: 5 givenname: Weisi orcidid: 0000-0001-9866-1947 surname: Lin fullname: Lin, Weisi email: wslin@ntu.edu.sg organization: School of Computer Science Engineering, Nanyang Technological University, Singapore – sequence: 6 givenname: Qingming orcidid: 0000-0001-7542-296X surname: Huang fullname: Huang, Qingming email: qmhuang@ucas.ac.cn organization: School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing, China |
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| SubjectTerms | Algorithms co-saliency detection Computer simulation depth attribute Feature extraction Image acquisition Image color analysis Image detection Imaging Integrated circuit modeling inter-image correspondence RGBD saliency detection Salience Saliency detection Salient object spatiotemporal constraint video saliency detection Visual perception Visual systems Visualization |
| Title | Review of Visual Saliency Detection With Comprehensive Information |
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