Predicting image influence on visual saliency distribution: the focal and ambient dichotomy

The computational modelling of visual attention relies entirely on visual fixations that are collected during eye-tracking experiments. Although all fixations are assumed to follow the same attention paradigm, some studies suggest the existence of two visual processing modes, called ambient and focal. In this paper, we present the high discrepancy between focal and ambient saliency maps and propose an automatic method for inferring the degree of focalness of an image. This method opens new avenues for the computational modelling of saliency models and their benchmarking.

[1]  Shuo Wang,et al.  Predicting human gaze beyond pixels. , 2014, Journal of vision.

[2]  Patrick Le Callet,et al.  A coherent computational approach to model bottom-up visual attention , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Gert Kootstra,et al.  Predicting Eye Fixations on Complex Visual Stimuli Using Local Symmetry , 2011, Cognitive Computation.

[4]  C. Trevarthen,et al.  Two mechanisms of vision in primates , 1968, Psychologische Forschung.

[5]  Giuseppe Boccignone,et al.  Modelling eye-movement control via a constrained search approach , 2011, 3rd European Workshop on Visual Information Processing.

[6]  Antoine Coutrot,et al.  Scanpath modeling and classification with hidden Markov models , 2017, Behavior Research Methods.

[7]  B. Velichkovsky,et al.  Two Visual Systems and Their Eye Movements: Evidence from Static and Dynamic Scene Perception , 2005 .

[8]  B. Velichkovsky,et al.  On the control of visual fixation durations in free viewing of complex images , 2011, Attention, perception & psychophysics.

[9]  T. Smith,et al.  Attentional synchrony and the influence of viewing task on gaze behavior in static and dynamic scenes. , 2013, Journal of vision.

[10]  D. S. Wooding,et al.  Fixation maps: quantifying eye-movement traces , 2002, ETRA.

[11]  Nicolas Riche,et al.  Saliency and Human Fixations: State-of-the-Art and Study of Comparison Metrics , 2013, 2013 IEEE International Conference on Computer Vision.

[12]  John K. Tsotsos,et al.  Saliency, attention, and visual search: an information theoretic approach. , 2009, Journal of vision.

[13]  Noel E. O'Connor,et al.  SalGAN: Visual Saliency Prediction with Generative Adversarial Networks , 2017, ArXiv.

[14]  Noel E. O'Connor,et al.  Shallow and Deep Convolutional Networks for Saliency Prediction , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Ali Borji,et al.  CAT2000: A Large Scale Fixation Dataset for Boosting Saliency Research , 2015, ArXiv.

[16]  Arzu Çöltekin,et al.  High-Level Gaze Metrics From Map Viewing - Charting Ambient/Focal Visual Attention , 2014, ET4S@GIScience.

[17]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[18]  Frédo Durand,et al.  Learning to predict where humans look , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[19]  Pat Hanrahan,et al.  Gaze Data for the Analysis of Attention in Feature Films , 2017, TAP.

[20]  Ali Borji,et al.  State-of-the-Art in Visual Attention Modeling , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Zhi Liu,et al.  Saccadic model of eye movements for free-viewing condition , 2015, Vision Research.

[22]  Matthias Bethge,et al.  Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet , 2014, ICLR.

[23]  William R. Mathew,et al.  Color as a Science , 2005 .

[24]  Qi Zhao,et al.  Webpage Saliency , 2014, ECCV.

[25]  Thierry Baccino,et al.  New insights into ambient and focal visual fixations using an automatic classification algorithm , 2011, i-Perception.

[26]  Rita Cucchiara,et al.  Multi-level Net: A Visual Saliency Prediction Model , 2016, ECCV Workshops.

[27]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[28]  Matthias Bethge,et al.  DeepGaze II: Reading fixations from deep features trained on object recognition , 2016, ArXiv.

[29]  Kitsuchart Pasupa,et al.  Learning to Predict Where People Look with Tensor-Based Multi-view Learning , 2015, ICONIP.

[30]  B. Velichkovsky,et al.  Time course of information processing during scene perception: The relationship between saccade amplitude and fixation duration , 2005 .

[31]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[32]  O. Meur,et al.  Introducing context-dependent and spatially-variant viewing biases in saccadic models , 2016, Vision Research.

[33]  Thierry Baccino,et al.  Methods for comparing scanpaths and saliency maps: strengths and weaknesses , 2012, Behavior Research Methods.

[34]  Joseph H. Goldberg,et al.  Identifying fixations and saccades in eye-tracking protocols , 2000, ETRA.

[35]  Giuseppe Boccignone,et al.  Modelling gaze shift as a constrained random walk , 2004 .