Review of Visual Saliency Detection With Comprehensive Information

By Runmin Cong, Jianjun Lei, Huan Fu, Ming-Ming Cheng, Weisi Lin, Qingming Huang. IEEE transactions on circuits and systems for video technology (Print), 2018

384 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Sensory Systems

View Paper: https://www.semanticscholar.org/paper/100b5cffe91d0a2c6b0ea049acaac437fb0f7ec4

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Citations in the corpus

Global Context-Aware Progressive Aggregation Network for Salient Object Detection (2020, background): “3 to emphasize the precision over re-call, as suggested in the previous work (Cong et al. 2018b).”
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks (2019, background): “Only a limited number of datasets (1∼4) have been benchmarked in recent papers [39], [56] (Table II).”
SwinNet: Swin Transformer Drives Edge-Aware RGB-D and RGB-T Salient Object Detection (2022, neutral): “As described in the SOD review [1], SOD has been extended from RGB image [2]–[4] to RGB-D image [5], [6], a group of images [7], [8] and video [9], [10].”
Transformers in Medical Imaging: A Survey (2022, methodology): “We have further divided interpretable models into saliency-based [228] and Grad-CAM [229] based visualization approaches.”
Interactive Two-Stream Decoder for Accurate and Fast Saliency Detection (2020, background): “For other researches please refer the recent survey [2, 6, 34, 23, 33] for more details.”
Salient Object Detection in the Deep Learning Era: An In-Depth Survey (2019, methodology): “[45] review several extended SOD tasks including RGB-D SOD, co-saliency detection and video SOD.”
Shifting More Attention to Video Salient Object Detection (2019, background): “More recently, deep learning based VSOD models [31, 38, 39, 41, 67, 68, 76] have gained more attention inspired by the success of applying deep neural networks on image saliency detection [13–15,32,50,71,72,86,88–90,94].”
Nested Network With Two-Stream Pyramid for Salient Object Detection in Optical Remote Sensing Images (2019, background): “The last decades have witnessed the remarkable progress of saliency detection for nature scene image, especially including the deep learning-based methods with highly competitive performance [7].”