A Novel Multiresolution Spatiotemporal Saliency Detection Model and Its Applications in Image and Video Compression

By Chenlei Guo, Liming Zhang. IEEE Transactions on Image Processing, 2010

978 citations recorded by the source corpus.

Subfields: Information Systems and Management

View Paper: https://www.semanticscholar.org/paper/01825573781674bcf85d0f5d2ec456842f75ad3c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems and Management

1. Dacheng Tao
2. Jianbing Shen
3. Haibin Ling
4. Wenguan Wang
5. Ali Borji
6. Ming-Ming Cheng
7. Laurent Itti

Citations in the corpus

• Revisiting Video Saliency Prediction in the Deep Learning Era (2021, methodology): “We compare our model with sixteen dynamic saliency models including: PQFT [10], Seo et al. [11], Rudoy et al. [12], Hou et al. [13], Fang et al. [14], OBDL [15], AWS-D [16], PMES [17], PIM-ZEN [18], MAM [19], PIM-MCS [20], MCSDM [21], MSM-SM [22], PNSP-CS [23], OM-CNN [24], and Two-stream [25].”
• Consistent Video Saliency Using Local Gradient Flow Optimization and Global Refinement (2015, background): “Digital Object Identifier 10.1109/TIP.2015.2460013 segmentation [35], [36], image retargeting [13], [23], [24], object recognition and detection [31], [34], [42], video compression [41] and summarization [30], [32].”
• Salient Object Detection: A Benchmark (2015, background): “compression [31], [32], video summarization [33]–[35], photo collage/media re-targeting/cropping/thumb-nailing [20],”
• A Benchmark of Computational Models of Saliency to Predict Human Fixations (2012, background): “Zhang et al. proposed the SUN saliency model 6 [87] using natural statistics based on a Bayesian framework to estimate the probability of a target at every location.”
• Learning Unsupervised Video Object Segmentation Through Visual Attention (2019, methodology): “Early attention models [42, 90, 19, 6, 22, 25, 36, 15, 20, 26, 61, 22] are based on biologically-inspired features (color, edge, optical flow, etc .) and cognitive theories about visual attention (attention shift [39], feature integration theory [68], guided search [82], etc .).”
• Shifting More Attention to Video Salient Object Detection (2019, background): “…the underlying mechanism behind HVS during free-viewing in general and instrumental to a wide range of real-world applications, e.g ., video segmentation [74, 83], video captioning [57], video compression [27, 29], autonomous driving [91], robotic interaction [82], weakly supervised attention [95].”
• Visual Saliency Based on Scale-Space Analysis in the Frequency Domain (2013, neutral): “F”
• Gaze Prediction in Dynamic 360° Immersive Videos (2018, background): “Some work also attempts to use low-level appearance and motion cues as inputs or extend deep learning approaches to more complex scenarios such as stereo images [9] or videos [4] [7] [11] [31] [35] [32] [16] [12] [6] [18].”
• Deeply Supervised Salient Object Detection with Short Connections (2016, background): “Such a useful property allows salient object detection to commonly serve as the first step to a variety of computer vision applications including image and video compression [2], [3], image segmentation [4], content-aware image editing [5], [6], object recognition [7], weakly supervsied segmantic se”
• State-of-the-Art in Visual Attention Modeling (2013, background): “[157] proposed a quaternion representation of an image combining intensity, color, and motion features.”
• Generative Adversarial Networks for Extreme Learned Image Compression (2018, neutral): “Finally, related to our SC mode, spatially allocating bi-trate based on saliency of image content has a long history in the context of engineered compression algorithms, see, e.g.,, (Stella & Lisin, 2009; Guo & Zhang, 2010; Gupta et al., 2013).”
• Non-local Deep Features for Salient Object Detection (2017, background): “Several applications bene-fit from saliency detection including image and video compression [14], context aware image re-targeting [25], scene parsing [50], image resizing [3], object detection [44] and segmentation [33].”
• Quantitative Analysis of Human-Model Agreement in Visual Saliency Modeling: A Comparative Study (2013, neutral): “[45] 2009 I M 400 × 400 26 Yin Li: Yin Li et al.”
• JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection (2020, background): “It has many useful applications, including object segmentation and recognition [27, 32, 39, 51, 70, 79], image/video compression [24], video detection/summarization [19, 41], content-based image editing [14, 23, 42, 57, 63], informative common object discovery [71,72], and image retrieval [8,22,37].”
• Revisiting Video Saliency: A Large-Scale Benchmark and a New Model (2018, background): “We compare our model with nine dynamic saliency models: PQFT [16], Seo et al .”