Directing user attention via visual flow on web designs

We present a novel approach that allows web designers to easily direct user attention via visual flow on web designs. By collecting and analyzing users' eye gaze data on real-world webpages under the task-driven condition, we build two user attention models that characterize user attention patterns between a pair of page components. These models enable a novel web design interaction for designers to easily create a visual flow to guide users' eyes (i.e., direct user attention along a given path) through a web design with minimal effort. In particular, given an existing web design as well as a designer-specified path over a subset of page components, our approach automatically optimizes the web design so that the resulting design can direct users' attention to move along the input path. We have tested our approach on various web designs of different categories. Results show that our approach can effectively guide user attention through the web design according to the designer's high-level specification.

[1]  W H LauRynson,et al.  Directing user attention via visual flow on web designs , 2016 .

[2]  Vladlen Koltun,et al.  Computer-generated residential building layouts , 2010, SIGGRAPH 2010.

[3]  C. Koch,et al.  A saliency-based search mechanism for overt and covert shifts of visual attention , 2000, Vision Research.

[4]  Ranjitha Kumar,et al.  Webzeitgeist: design mining the web , 2013, CHI.

[5]  Jakob Nielsen,et al.  Eyetracking Web Usability , 2009 .

[6]  Rynson W. H. Lau,et al.  Look over here , 2014, ACM Trans. Graph..

[7]  Wilson S. Geisler,et al.  Simple summation rule for optimal fixation selection in visual search , 2009, Vision Research.

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

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

[10]  Aaron Hertzmann,et al.  Learning Layouts for Single-PageGraphic Designs , 2014, IEEE Transactions on Visualization and Computer Graphics.

[11]  S R Ellis,et al.  Statistical Dependency in Visual Scanning , 1986, Human factors.

[12]  V. Goffaux,et al.  The horizontal tuning of face perception relies on the processing of intermediate and high spatial frequencies. , 2011, Journal of vision.

[13]  Yeliz Yesilada,et al.  Eye tracking scanpath analysis techniques on web pages: A survey, evaluation and comparison , 2015 .

[14]  Yuan Yao,et al.  Simulating human saccadic scanpaths on natural images , 2011, CVPR 2011.

[15]  Stephen Lin,et al.  Semantically-Based Human Scanpath Estimation with HMMs , 2013, 2013 IEEE International Conference on Computer Vision.

[16]  Chi-Keung Tang,et al.  Make it home: automatic optimization of furniture arrangement , 2011, SIGGRAPH 2011.

[17]  N. C. Silver,et al.  Averaging Correlation Coefficients: Should Fishers z Transformation Be Used? , 1987 .

[18]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

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

[20]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[21]  Rynson W. H. Lau,et al.  Automatic stylistic manga layout , 2012, ACM Trans. Graph..

[22]  Chi-Keung Tang,et al.  Make it home: automatic optimization of furniture arrangement , 2011, ACM Trans. Graph..

[23]  Zhihao Yang,et al.  Semisupervised Learning Based Disease-Symptom and Symptom-Therapeutic Substance Relation Extraction from Biomedical Literature , 2016, BioMed research international.

[24]  Alexander C. Schütz,et al.  Eye movements and perception: a selective review. , 2011, Journal of vision.

[25]  W. K. Hastings,et al.  Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .

[26]  Aaron Hertzmann,et al.  Color compatibility from large datasets , 2011, ACM Trans. Graph..

[27]  Ranjitha Kumar,et al.  Bricolage: example-based retargeting for web design , 2011, CHI.

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