Predicting visual fixations on video based on low-level visual features.
By O. Le Meur, P. Le Callet, Dominique Barba. Vision Research, 2007
338 citations recorded by the source corpus.
Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition, Human-Computer Interaction
View Paper: https://www.semanticscholar.org/paper/8e1b4866beb743ef9272969d60e08fe39725c023
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cognitive Neuroscience
- Frédo Durand
- Tilke Judd
- Antonio Torralba
- Deng-Ping Fan
- Peyman Milanfar
- Hae Jong Seo
- Matei Mancas
- Yuchao Dai
In Computer Vision and Pattern Recognition
- Ling Shao
- Ming-Ming Cheng
- Deng-Ping Fan
- Jianbing Shen
- Ming-Hsuan Yang
- Luc Van Gool
- Ali Borji
- Wenguan Wang
In Human-Computer Interaction
- Antonio Torralba
- Tilke Judd
- Frédo Durand
- Matei Mancas
- Yuchao Dai
- Peyman Milanfar
- Deng-Ping Fan
- Matthieu Duvinage
Citations in the corpus
- The Evolution of Video Quality Measurement: From PSNR to Hybrid Metrics (2008, neutral): “still a relatively new area of research; some recent papers h ave addressed the issue in more detail [42]–[45].”
- Saliency and Human Fixations: State-of-the-Art and Study of Comparison Metrics (2013, methodology): “Many authors like [24], [26] and [17] already used this metric to compare saliency maps with human eyes fixations.”
- Quantitative Analysis of Human-Model Agreement in Visual Saliency Modeling: A Comparative Study (2013, neutral): “[58], [34] 2006 B X W × H 17 GBVS: Harel et al.”
- State-of-the-Art in Visual Attention Modeling (2013, neutral): “[138] extended this model to spatio-temporal”
- Learning to predict where humans look (2009, neutral): “as noted for other datasets as well by [23] [11].”
- A Benchmark of Computational Models of Saliency to Predict Human Fixations (2012, methodology): “[41] [46] adapted the Koch-Ullman model to include the features of contrast sensitivity functions, perceptual decomposition, visual masking, and center-surround interactions.”
- Static and space-time visual saliency detection by self-resemblance. (2009, methodology): “To avoid some drawbacks of earlier methods [17], [25], we do not combi ne saliency maps from each color channel linearly and directly.”
- Simultaneously Localize, Segment and Rank the Camouflaged Objects (2021, methodology): “For the discriminative region localization, we adopt the widely used fixation prediction evaluation metrics including Similarity (SIM ) [19], Linear Correlation Coefficient (CC) [23], Earth Mover’s Distance (EMD) [38], Kullback–Leibler Divergence (KLD) [21], Normalized Scanpath Saliency (NSS) [33], ”