A coherent computational approach to model bottom-up visual attention

By O. Le Meur, P. Le Callet, Dominique Barba, Dominique Thoreau. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006

698 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/f30fed23882675422aada3bd60ed7d1ae84fcb09

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Citations in the corpus

What Do Different Evaluation Metrics Tell Us About Saliency Models? (2016, background): “The effect of center bias on model evaluation has received much attention [13], [22], [47], [60], [67], [78], [79], [93].”
Quantitative Analysis of Human-Model Agreement in Visual Saliency Modeling: A Comparative Study (2013, background): “Le Meur: Le Meur et al. [58], [34] 2006 B X W × H 17 GBVS: Harel et al. [26] 2006 I M W × H 18 HouCVPR: Hou et al. [40] 2007 I M 64 × 64 19 Rarity-L: Local Rarity [42] 2007 I M W × H 20 Rarity-G: Global Rarity [42] 2007 I M W × H 21 HouNIPS: Hou et al. [41] 2008 I M W × H 22 Kootstra: Kootstra & Sho”
Revisiting Video Saliency: A Large-Scale Benchmark and a New Model (2018, background): “Early static saliency models [36, 69, 15, 7, 18, 22, 33, 63] are mostly based on the contrast assumption that conspicuous visual features “pop-out” and involuntarily capture attention (see [5, 6] for review).”
Deep Visual Attention Prediction (2017, background): “These models can be generally classiﬁed into two main categories as bottom-up approaches [8], [9] and top-down approaches [10], [11], [12].”
Saliency Detection by Multitask Sparsity Pursuit (2012, methodology): “Le Meur et al. [25], [27] modeled the bottom-up visual attention using a coherent computational approach.”
Visual Saliency Based on Scale-Space Analysis in the Frequency Domain (2013, neutral): “[3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15],”
A Benchmark of Computational Models of Saliency to Predict Human Fixations (2012, methodology): “To facilitate comparisons, several authors have produced openly available databases of images with fixations from eye tracking experiments [41] [42] [10] [6] [37] [61] and executable code of models that others can use.”
Methods for comparing scanpaths and saliency maps: strengths and weaknesses (2012, methodology): “We compare the performance of four state-of-the-art models: Itti ’ s model (Itti, Koch, & Niebur, 1998), Le Meur ’ s model (Le Meur et al., 2006), Bruce ’ s model (Bruce & Tsotsos, 2009), and Judd ’ s model (Judd et al., 2009).”
Revisiting Video Saliency Prediction in the Deep Learning Era (2021, background): “Early static saliency models [27], [45], [46], [47], [48], [49], [50], [51] are mostly concerned with the bottom-up visual attention mechanism (see [52] for detailed review).”
Learning to Detect A Salient Object (2007, methodology): “Most existing visual attention approaches are based on the bottom-up computational framework [3, 6, 8, 9, 10, 11, 19, 25] because visual attention is in general unconsciously driven by low-level stimulus in the scene such as intensity, contrast, and motion.”
Creativity, attention and the memory for brands: an outdoor advertising field study (2015, methodology): “Researchers find that bottom-up factors have the greatest influence on attention immediately after their presentation as people orient themselves to the situation or task (Donk and Soesman 2010; Le Meur et al. 2006).”
Attentive Systems: A Survey (2017, background): “In Meur et al. (2006), the most important salient parts of the picture are cropped to fit the limited display size. Marchesotti et al. (2009) propose a framework for image thumbnailing based on visual similarity.”
Predicting visual fixations on video based on low-level visual features. (2007, methodology): “Moreover, in a previous study dealing with still color pictures (Le Meur et al., 2006), the similarity degree for different viewing times (4, 10 and 14 seconds) was evaluated.”
Display adaptive tone mapping (2008, methodology): “Weighing ROI by the distance from the frame center or employing attention models [Le Meur et al. 2006] can be another choice.”
Consistent Video Saliency Using Local Gradient Flow Optimization and Global Refinement (2015, background): “Saliency detection is originally a task of predicting scene locations where a human observer may ﬁxate [3], [9].”
Context-aware saliency detection (2010, background): “The biologically-motivated algorithms for saliency estimation [9, 8, 24, 3, 6, 12] are based on principle (1).”