VISUAL SALIENCY DOES NOT ACCOUNT FOR EYE MOVEMENTS DURING VISUAL SEARCH IN REAL-WORLD SCENES

By J. Henderson, James R. Brockmole, Monica S. Castelhano, Michael L. Mack. 2007

477 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/e230448258251e34056bdfb79d73402d526ce750

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Citations in the corpus

Two forms of scene memory guide visual search: Memory for scene context and memory for the binding of target object to scene location (2009, background): “The latter data add to a large body of literature demonstrating that eye movements when viewing natural scenes are strongly driven by—and often dominated by—cognitive factors, such as memory and top-down goals (Brockmole & Henderson, 2006a , b ; Castelhano & Henderson, 2007 ; Henderson, Brockmole”
Predicting human gaze using low-level saliency combined with face detection (2007, background): “, in search tasks [7, 8], strong contextual effects [9], or in free-viewing of images without clearly isolated objects, such as”
Comparing scanpaths during scene encoding and recognition : A multi-dimensional approach (2012, background): “11 meaningful elements drawing attention; Itti & Koch, 2001; Henderson et al., 2007).”
Modeling Search for People in 900 Scenes: A combined source model of eye guidance (2009, unknown): “We therefore derived a measure to quantify the proportion of inter-observer agreement that was independent of the particular scene’s content (see also Foulsham & Underwood, 2008; Henderson, Brockmole, Castelhano, & Mack, 2007).”
A Benchmark of Computational Models of Saliency to Predict Human Fixations (2012, methodology): “[29] provide evidence that top-down information dominates real-world image search processes, such that the influence of low-level salience information on search guidance is minimal and others show that context is important [71] [51].”
Eye guidance in natural vision: Reinterpreting salience (2011, background): “…these tendencies into models of fixation selection dramatically improves the predictive power of the model (Tatler & Vincent, 2009); indeed, these motor biases alone predicted fixation selection better than a model based on homogenous salience computation or homogenous edge feature extraction.”
The 35th Sir Frederick Bartlett Lecture: Eye movements and attention in reading, scene perception, and visual search (2009, background): “Saliency is not the only factor involved in determining where to look, and there are questions about how important it is (Foulsham & Underwood, 2008 ; Henderson, Brockmole, Castelhano, & Mack, 2007 ).”
Tracking Restorative Components: Patterns in Eye Movements as a Consequence of a Restorative Rating Task (2013, background): “Instead, participants focused on the objects of importance for assessing the park images (see Henderson et al., 2007).”
Clustering of Gaze During Dynamic Scene Viewing is Predicted by Motion (2011, background): “As visual features such as luminance, contrast, and edge density in scenes have been shown to correlate with the rated semantic informativeness of a location [8], gaze may be guided toward an object due to its cognitive relevance, and the corresponding peak in Cogn Comput”
Computational visual attention systems and their cognitive foundations: A survey (2010, methodology): “Scene classification and global localization based on salient landmarks is presented in Siagian and Itti [2009]. In addition to the landmarks, the authors use the “gist” of the scene, a feature vector, which captures the appearance of the scene, to obtain a coarse localization hypothesis.”