Review of Visual Saliency Detection With Comprehensive Information

• Published in: IEEE transactions on circuits and systems for video technology Vol. 29; no. 10; pp. 2941 - 2959
• Main Authors: Cong, Runmin, Lei, Jianjun, Fu, Huazhu, Cheng, Ming-Ming, Lin, Weisi, Huang, Qingming
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 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
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2018.2870832

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.