Sandeep Vidyapu
Skip Abstract Section
Abstract
User attentional analyses on web elements help in synthesis and rendering of webpages. However, majority of the existing analyses are limited in incorporating the intrinsic visual features of text and images. This study aimed to analyze the influence of elements’ visual features (font-size, font-family, color, etc., for text; and brightness, color, intensity, etc., for images) besides their position on users’ free-viewing visual attention. The investigation includes: (i) user’s position-based attention allocation on text and image web elements, (ii) identification of informative visual features with respect to the attention, (iii) performance of informative visual features in predicting the ordinal visual attention (fixation-indices). Towards the study, an eye-tracking experiment was conducted with 42 participants on 36 real-world webpages. The analyses revealed: (i) Though users predominantly allocate the initial attention to MiddleCenter}, MiddleLeft, TopCenter, TopLeft regions, the elements in Right and Bottom regions are not completely ignored; (ii) Space-related (column-gap, line-height, padding) and font Size-related (font-size, font-weight) intrinsic text features, and Mid-level Color Histogram intrinsic image features are informative, while position and size are informative for both the types; (iii) the informative visual features predict the ordinal visual attention on an element with 90% average accuracy and 70% micro-F1 score. Our approach finds applications in element-granular web-designing and user attention prediction.
- Sareh Aghaei, Mohammad Ali Nematbakhsh, and Hadi Khosravi Farsani. 2012. Evolution of the world wide web: From WEB 1.0 TO WEB 4.0. Int. J. Web Seman. Technol. 3, 1 (2012), 1. DOI:https://doi.org/10.5121/ijwest.2012.3101Google Scholar
- Hyejin Bang and Bartosz W. Wojdynski. 2016. Tracking users’ visual attention and responses to personalized advertising based on task cognitive demand. Comput. Hum. Behav. 55 (2016), 867--876. DOI:https://doi.org/10.1016/j.chb.2015.10.025Google ScholarDigital Library
- Michael L. Bernard, Barbara S. Chaparro, Melissa M. Mills, and Charles G. Halcomb. 2003. Comparing the effects of text size and format on the readibility of computer-displayed Times New Roman and Arial text. Int. J. Hum.-Comput. Stud. 59, 6 (2003), 823--835. DOI:https://doi.org/10.1016/S1071-5819(03)00121-6Google ScholarDigital Library
- Torsten Betz, Tim C. Kietzmann, Niklas Wilming, and Peter Koenig. 2010. Investigating task-dependent top-down effects on overt visual attention. J. Vis. 10, 3 (2010), 15--15. DOI:https://doi.org/10.1167/10.3.15Google ScholarCross Ref
- Ali Borji and Laurent Itti. 2013. State-of-the-art in visual attention modeling. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1 (2013), 185--207. DOI:https://doi.org/10.1109/TPAMI.2012.89Google ScholarDigital Library
- Bernhard E. Boser, Isabelle M. Guyon, and Vladimir N. Vapnik. 1992. A training algorithm for optimal margin classifiers. In Proceedings of the 5th Workshop on Computational Learning Theory. ACM, New York, NY, 144--152. DOI:https://doi.org/10.1145/130385.130401Google Scholar
- Steven Bradley. 2011. 3 Design Layouts: Gutenberg Diagram, Z-Pattern, and F-Pattern. Retrieved from http://vanseodesign.com/web-design/3-design-layouts/.Google Scholar
- A. Buchner and N. Baumgartner. 2007. Text--background polarity affects performance irrespective of ambient illumination and colour contrast. Ergonomics 50, 7 (2007), 1036--1063. DOI:https://doi.org/10.1080/00140130701306413Google ScholarCross Ref
- Georg Buscher, Edward Cutrell, and Meredith Ringel Morris. 2009. What do you see when you’re surfing?: Using eye tracking to predict salient regions of web pages. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, NY, 21--30. DOI:https://doi.org/10.1145/1518701.1518705Google ScholarDigital Library
- Michael D. Byrne, John R. Anderson, Scott Douglass, and Michael Matessa. 1999. Eye tracking the visual search of click-down menus. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, New York, NY, 402--409. DOI:https://doi.org/10.1145/302979.303118Google ScholarDigital Library
- Chih-Chung Chang and Chih-Jen Lin. 2011. LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 3 (2011), 27. DOI:https://doi.org/10.1145/1961189.1961199Google ScholarDigital Library
- Jacob Cohen. 1960. A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 20, 1 (1960), 37--46. DOI:https://doi.org/10.1177/0013164460020000104Google ScholarCross Ref
- World Wide Web Consortium et al. 2004. Document Object Model (DOM) Level 3 Core Specification. W3C. Retrieved from https://www.w3.org/TR/2004/REC-DOM-Level-3-Core-20040407/.Google Scholar
- Corinna Cortes and Vladimir Vapnik. 1995. Support-vector networks. Mach. Learn. 20, 3 (1995), 273--297. DOI:https://doi.org/10.1007/BF00994018Google ScholarCross Ref
- Paulo Cortez. 2016. rminer: Data Mining Classification and Regression Methods. R Foundation for Statistical Computing. Retrieved from https://CRAN.R-project.org/package=rminer R package version 1.4.2.Google Scholar
- Dianne Cyr, Milena Head, and Hector Larios. 2010. Colour appeal in website design within and across cultures: A multi-method evaluation. Int. J. Hum.-Comput. Stud. 68, 1–2 (2010), 1--21. DOI:https://doi.org/10.1016/j.ijhcs.2009.08.005Google ScholarDigital Library
- Soussan Djamasbi, Marisa Siegel, and Tom Tullis. 2010. Generation Y, web design, and eye tracking. Int. J. Hum.-Comput. Stud. 68, 5 (2010), 307--323. DOI:https://doi.org/10.1016/j.ijhcs.2009.12.006Google ScholarDigital Library
- Mary C. Dyson. 2004. How physical text layout affects reading from screen. Behav. Inf. Technol. 23, 6 (2004), 377--393. DOI:https://doi.org/10.1080/01449290410001715714Google ScholarCross Ref
- Sukru Eraslan, Yeliz Yesilada, and Simon Harper. 2016. Scanpath trend analysis on web pages: Clustering eye tracking scanpaths. ACM Trans. Web 10, 4 (2016), 20. DOI:https://doi.org/10.1145/2970818Google ScholarDigital Library
- Pete Faraday. 2000. Visually critiquing web pages. In Proceedings of the Multimedia’99. Springer. Retrieved from http://facweb.cs.depaul.edu/cmiller/faraday/Faraday.htm.Google ScholarCross Ref
- James Fogarty, Scott E. Hudson, Christopher G. Atkeson, Daniel Avrahami, Jodi Forlizzi, Sara Kiesler, Johnny C. Lee, and Jie Yang. 2005. Predicting human interruptibility with sensors. ACM Trans. Comput.-Hum. Interact. 12, 1 (2005), 119--146. DOI:https://doi.org/10.1145/1057237.1057243Google ScholarDigital Library
- Max Kuhn. Contributions from Jed Wing, Steve Weston, Andre Williams, Chris Keefer, Allan Engelhardt, Tony Cooper, Zachary Mayer, Brenton Kenkel, the R Core Team, Michael Benesty, Reynald Lescarbeau, Andrew Ziem, Luca Scrucca, Yuan Tang, Can Candan, and Tyler Hunt.2018. caret: Classification and Regression Training. R Foundation for Statistical Computing. Retrieved from https://CRAN.R-project.org/package=caret R package version 6.0-79.Google Scholar
- William J. Gibbs and Ronan S. Bernas. 2009. Visual attention in newspaper versus TV-oriented news websites. J. Usab. Stud. 4, 4 (2009), 147--165. Retrieved from http://uxpajournal.org/visual-attention-in-newspaper-versus-tv-oriented-news-websites/.Google ScholarDigital Library
- Joseph H. Goldberg, Mark J. Stimson, Marion Lewenstein, Neil Scott, and Anna M. Wichansky. 2002. Eye tracking in web search tasks: Design implications. In Proceedings of the Symposium on Eye Tracking Research 8 Applications. ACM, NY, 51--58. DOI:https://doi.org/10.1145/507072.507082Google Scholar
- Rebecca Anne Grier. 2004. Visual Attention and Web Design. Ph.D. Dissertation. University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1092767744.Google Scholar
- Antonio Gulli and Alessio Signorini. 2005. The indexable web is more than 11.5 billion pages. In Special Interest Tracks and Posters of the 14th International Conference on World Wide Web. ACM, New York, NY, 902--903. DOI:https://doi.org/10.1145/1062745.1062789Google ScholarDigital Library
- John M. Henderson and Andrew Hollingworth. 1999. High-level scene perception. Ann. Rev. Psychol. 50, 1 (1999), 243--271. DOI:https://doi.org/10.1146/annurev.psych.50.1.243Google ScholarCross Ref
- Yu-Chen Hsieh and Kuo-Hsiang Chen. 2011. How different information types affect viewer’s attention on internet advertising. Comput. Hum. Behav. 27, 2 (2011), 935--945. DOI:https://doi.org/10.1016/j.chb.2010.11.019Google ScholarDigital Library
- Yu-Chen Hsieh, Kuo-Hsiang Chen, and Min-Yuan Ma. 2012. Retain viewer’s attention on banner ad by manipulating information type of the content. Comput. Hum. Behav. 28, 5 (2012), 1692--1699. DOI:https://doi.org/10.1016/j.chb.2012.04.008Google ScholarDigital Library
- Chih-Wei Hsu and Chih-Jen Lin. 2002. A comparison of methods for multiclass support vector machines. IEEE Trans. Neur. Netw. 13, 2 (2002), 415--425. DOI:https://doi.org/10.1109/72.991427Google ScholarDigital Library
- Laurent Itti, Christof Koch, and Ernst Niebur. 1998. A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20, 11 (1998), 1254--1259. DOI:https://doi.org/10.1109/34.730558Google ScholarDigital Library
- Kurt M. Joseph, Benjamin A. Knott, and Rebecca A. Grier. 2002. The effects of bold text on visual search of form fields. Proc. Hum. Fact. Ergon. Soc. Meet. 46, 4 (2002), 583--587. DOI:https://doi.org/10.1177/154193120204600403Google Scholar
- Marcel A. Just and Patricia A. Carpenter. 1980. A theory of reading: From eye fixations to comprehension. Psychol. Rev. 87, 4 (1980), 329. DOI:https://doi.org/10.1037/0033-295X.87.4.329Google ScholarCross Ref
- Young Sik Kang and Heeseok Lee. 2010. Understanding the role of an IT artifact in online service continuance: An extended perspective of user satisfaction. Comput. Hum. Behav. 26, 3 (2010), 353--364. DOI:https://doi.org/10.1016/j.chb.2009.11.006Google ScholarDigital Library
- Alexandros Karatzoglou, Alex Smola, Kurt Hornik, and Achim Zeileis. 2004. kernlab—An S4 package for kernel methods in R. J. Statist. Softw. 11, 9 (2004), 1--20. Retrieved from http://www.jstatsoft.org/v11/i09/.Google ScholarCross Ref
- Keith S. Karn. 2000. “Saccade pickers” vs. “fixation pickers”: The effect of eye tracking instrumentation on research. In Proceedings of the Symposium on Eye Tracking Research 8 Applications. ACM, New York, NY, 87--88. DOI:https://doi.org/10.1145/355017.355030Google ScholarDigital Library
- Conor Kelton, Jihoon Ryoo, Aruna Balasubramanian, and Samir R. Das. 2017. Improving user perceived page load time using gaze. In Proceedings of the 14th USENIX Conference on Networked Systems Design and Implementation (NSDI’17). USENIX Association, Berkeley, CA, 545--559. Retrieved from http://dl.acm.org/citation.cfm?id=3154630.3154675.Google ScholarDigital Library
- M. Lewenstein, G. Edwards, D. Tatar, and A. Devigal. 2006. Front page entry points (Initial analysis). Retrieved from https://issuu.com/kathyohlhaber/docs/eyetracking.Google Scholar
- Jian Li, Li Su, Bo Wu, Junbiao Pang, Chunfeng Wang, Zhe Wu, and Qingming Huang. 2016. Webpage saliency prediction with multi-features fusion. In Proceedings of the IEEE International Conference on Image Processing (ICIP’16). 674--678. DOI:https://doi.org/10.1109/ICIP.2016.7532442Google ScholarCross Ref
- Jonathan Ling and Paul van Schaik. 2002. The effect of text and background colour on visual search of Web pages. Displays 23, 5 (2002), 223--230. DOI:https://doi.org/10.1016/S0141-9382(02)00041-0Google ScholarCross Ref
- Jonathan Ling and Paul van Schaik. 2007. The influence of line spacing and text alignment on visual search of web pages. Displays 28, 2 (2007), 60--67. DOI:https://doi.org/10.1016/j.displa.2007.04.003Google ScholarCross Ref
- Sebastian Linxen, Silvia Heinz, Livia J. Müller, Alexandre N. Tuch, and Klaus Opwis. 2014. Mental models for web objects in different cultural settings. In CHI’14 Extended Abstracts on Human Factors in Computing Systems (CHI EA’14). ACM, New York, NY, 2557--2562. DOI:https://doi.org/10.1145/2559206.2581209Google Scholar
- Mary L. McHugh. 2012. Interrater reliability: The kappa statistic. Biochem. Medic. 22, 3 (2012), 276--282. DOI:https://doi.org/10.11613/BM.2012.031Google ScholarCross Ref
- Tom M. Mitchell et al. 1997. Machine Learning. McGraw Hill, Burr Ridge, IL.Google Scholar
- Jakob Nielsen. 1994. Estimating the number of subjects needed for a thinking aloud test. Int. J. Hum.-Comput. Stud. 41, 3 (1994), 385--397. DOI:https://doi.org/10.1006/ijhc.1994.1065Google ScholarDigital Library
- Jakob Nielsen. 2006. F-shaped pattern for reading web content. Alertbox: Current Issues in Web Usability Retrieved from https://www.nngroup.com/articles/f-shaped-pattern-reading-web-content-discovered/.Google Scholar
- Manos Papagelis and Dimitris Plexousakis. 2005. Qualitative analysis of user-based and item-based prediction algorithms for recommendation agents. Eng. Applic. Artif. Intell. 18, 7 (2005), 781--789. DOI:https://doi.org/10.1016/j.engappai.2005.06.010Google ScholarDigital Library
- Hyeonggyu Park, Sangil Lee, Minho Lee, Mun-Seon Chang, and Ho-Wan Kwak. 2016. Using eye movement data to infer human behavioral intentions. Comput. Hum. Behav. 63 (2016), 796--804. DOI:https://doi.org/10.1016/j.chb.2016.06.016Google ScholarDigital Library
- Siegmund Pastoor. 1990. Legibility and subjective preference for color combinations in text. Hum. Fact. 32, 2 (1990), 157--171. DOI:https://doi.org/10.1177/001872089003200204Google ScholarDigital Library
- Robert Pearson and Paul van Schaik. 2003. The effect of spatial layout of and link colour in web pages on performance in a visual search task and an interactive search task. Int. J. Hum.-Comput. Stud. 59, 3 (2003), 327--353. DOI:https://doi.org/10.1016/S1071-5819(03)00045-4Google ScholarDigital Library
- Rik Pieters and Luk Warlop. 1999. Visual attention during brand choice: The impact of time pressure and task motivation. Int. J. Res. Market. 16, 1 (1999), 1--16. DOI:https://doi.org/10.1016/S0167-8116(98)00022-6Google ScholarCross Ref
- Rik Pieters and Michel Wedel. 2004. Attention capture and transfer in advertising: Brand, pictorial, and text-size effects. J. Market. 68, 2 (2004), 36--50. DOI:https://doi.org/10.1509/jmkg.68.2.36.27794Google ScholarCross Ref
- Alex Poole and Linden J. Ball. 2006. Eye tracking in HCI and usability research. In Encyclopedia of Human Computer Interaction. IGI Global, 211--219. DOI:https://doi.org/10.4018/978-1-59140-562-7.ch034Google Scholar
- Matías Recabarren and Miguel Nussbaum. 2010. Exploring the feasibility of web form adaptation to users’ cultural dimension scores. User Model. User-Adapt. Interact. 20, 1 (2010), 87--108. DOI:https://doi.org/10.1007/s11257-010-9071-7Google ScholarDigital Library
- James A. Renshaw, Janet Finlay, and N. Webb. 2006. Getting a measure of satisfaction from eyetracking in practice. In CHI’06 Extended Abstracts. ACM, New York, NY, 1723--1726. DOI:https://doi.org/10.1145/1125451.1125772Google Scholar
- James A. Renshaw and N. Webb. 2007. Eye tracking in practice. In Proceedings of the 21st British HCI Group Conference on People and Computers: HCI...but not as we know it-Volume 2. BCS Learning 8 Development Ltd., Swindon, UK, 239--241. Retrieved from http://doi.acm.org/10.1145/1531407.1531485.Google Scholar
- Brahima Sanou. 2017. ICT Facts and Figures 2017. Retrieved from https://www.itu.int/en/ITU-D/Statistics/Pages/facts/default.aspx.Google Scholar
- Wei Shan, Guangling Sun, Xiaofei Zhou, and Zhi Liu. 2017. Two-stage transfer learning of end-to-end convolutional neural networks for webpage saliency prediction. In Proceedings of the International Conference on Intelligent Science and Big Data Engineering. Springer, Cham, Switzerland, 316--324. DOI:https://doi.org/10.1007/978-3-319-67777-4_27Google ScholarCross Ref
- Ming Shao and Yun Fu. 2016. Cross-modality feature learning through generic hierarchical hyperlingual-words. IEEE Trans. Neur. Netw. Learn. Syst. 28, 2 (2016), 451--463. DOI:https://doi.org/10.1109/TNNLS.2016.2517014Google ScholarCross Ref
- Chengyao Shen and Qi Zhao. 2014. Webpage saliency. In Proceedings of the International Conference on Computer Vision (ECCV’14). Springer, Cham, Switzerland, 33--46. DOI:https://doi.org/10.1007/978-3-319-10584-0_3Google ScholarCross Ref
- Sav Shrestha and Kelsi Lenz. 2007. Eye gaze patterns while searching vs. browsing a Website. Usab. News 9, 1 (2007), 17.Google Scholar
- Jeremiah D. Still. 2017. Web page attentional priority model. Cog. Technol. Work 19, 2–3 (2017), 363--374. DOI:https://doi.org/10.1007/s10111-017-0411-9Google Scholar
- Jeremiah D. Still. 2018. Web page visual hierarchy: Examining Faraday’s guidelines for entry points. Comput. Hum. Behav. 84 (2018), 352--359. DOI:https://doi.org/10.1016/j.chb.2018.03.014Google ScholarCross Ref
- Jeremiah D. Still and Christopher M. Masciocchi. 2010. A saliency model predicts fixations in web interfaces. In Proceedings of the 5th International Workshop on Model Driven Development of Advanced User Interfaces (MDDAUI’10). Citeseer, ACM, Atlanta, Georgia, 25. Retrieved from http://ceur-ws.org/Vol-617/.Google Scholar
- Barbara G. Tabachnick, Linda S. Fidell, and Steven J. Osterlind. 2001. Using Multivariate Statistics. Allyn and Bacon, Boston.Google Scholar
- Shuto Tamura and Keiko Sato. 2018. Age-related differences in visual search for color targets manipulated based on cone-contrast model. In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC’18). IEEE, 2992--2997. DOI:https://doi.org/10.1109/SMC.2018.00508Google ScholarCross Ref
- Terry Therneau and Beth Atkinson. 2018. rpart: Recursive Partitioning and Regression Trees. R Foundation for Statistical Computing. Retrieved from https://CRAN.R-project.org/package=rpart R package version 4.1-13.Google Scholar
- Nicholas Vanderschantz, Claire Timpany, David Whitehead, and Wendy Diane Carss. 2010. A small scale study into the effect that text 8 background colour has on processing and self-correction rates for childrens’ on-screen reading. Int. J. Book 7, 4 (2010), 75--88. DOI:https://doi.org/10.18848/1447-9516/CGP/v07i04/36841Google ScholarCross Ref
- Sandeep Vidyapu, Vijaya Saradhi Vedula, and Samit Bhattacharya. 2019. Quantitative visual attention prediction on webpage images using multiclass SVM. In Proceedings of the 11th ACM Symposium on Eye Tracking Research 8 Applications (ETRA’19). ACM, NY, 90:1--90:9. DOI:https://doi.org/10.1145/3317960.3321614Google Scholar
- Benjamin Wassermann, Adrian Hardt, and Gottfried Zimmermann. 2012. Generic gaze interaction events for web browsers using the eye tracker as input device. In Proceedings of the 21st International Conference on World Wide Web. Citeseer, 6. DOI:https://doi.org/10.13140/2.1.1871.9362Google Scholar
- Ian H. Witten, Eibe Frank, Mark A. Hall, and Christopher J. Pal. 2016. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann. DOI:https://doi.org/10.1016/c2009-0-19715-5Google ScholarDigital Library
- Jeremy M. Wolfe and Todd S. Horowitz. 2004. What attributes guide the deployment of visual attention and how do they do it? Nat. Rev. Neurosci. 5, 6 (2004), 495. DOI:https://doi.org/10.1038/nrn1411Google ScholarCross Ref
- Jian Wu, Shiquan Zhao, Victor S. Sheng, Jing Zhang, Chen Ye, Pengpeng Zhao, and Zhiming Cui. 2017. Weak-labeled active learning with conditional label dependence for multilabel image classification. IEEE Trans. Multim. 19, 6 (2017), 1156--1169. DOI:https://doi.org/10.1109/TMM.2017.2652065Google ScholarDigital Library
- Chenggang Yan, Liang Li, Chunjie Zhang, Bingtao Liu, Yongdong Zhang, and Qionghai Dai. 2019. Cross-modality bridging and knowledge transferring for image understanding. IEEE Trans. Multim. 21, 10 (2019), 2675--2685. DOI:https://doi.org/10.1109/TMM.2019.2903448Google ScholarDigital Library
- Chenggang Yan, Yunbin Tu, Xingzheng Wang, Yongbing Zhang, Xinhong Hao, Yongdong Zhang, and Qionghai Dai. 2019. STAT: Spatial-temporal attention mechanism for video captioning. IEEE Trans. Multim. 22, 1 (2019). DOI:https://doi.org/10.1109/TMM.2019.2924576Google Scholar
- Chenggang Yan, Hongtao Xie, Jianjun Chen, Zhengjun Zha, Xinhong Hao, Yongdong Zhang, and Qionghai Dai. 2018. A fast Uyghur text detector for complex background images. IEEE Trans. Multim. 20, 12 (2018), 3389--3398. DOI:https://doi.org/10.1109/TMM.2018.2838320Google ScholarDigital Library
- Chaitra Yangandul, Sachin Paryani, Madison Le, and Eakta Jain. 2018. How many words is a picture worth?: Attention allocation on thumbnails versus title text regions. In Proceedings of the ACM Symposium on Eye Tracking Research 8 Applications. ACM, New York, NY, 3. DOI:https://doi.org/10.1145/3204493.3204571Google ScholarDigital Library
- Alfred L. Yarbus. 1967. Eye movements during perception of complex objects. In Eye Movements and Vision. Springer, 171--211. DOI:https://doi.org/10.1007/978-1-4899-5379-7_8Google Scholar
- Panayiotis Zaphiris and Sri Kurniawan. 2007. Human Computer Interaction Research in Web Design and Evaluation. IGI Global. DOI:https://doi.org/10.4018/978-1-59904-246-6Google Scholar
Index Terms
- Investigating and Modeling the Web Elements’ Visual Feature Influence on Free-viewing Attention
Recommendations
Quantitative visual attention prediction on webpage images using multiclass SVM
ETRA '19: Proceedings of the 11th ACM Symposium on Eye Tracking Research & ApplicationsWebpage images---image elements on a webpage---are prominent to draw user attention. Modeling attention on webpage images helps in their synthesis and rendering. This paper presents a visual feature-based attention prediction model for webpage images. ...
Relevance of a feed-forward model of visual attention for goal-oriented and free-viewing tasks
A purely bottom-up model of visual attention is proposed and compared to five state-of-the-art models. The role of the low-level visual features is examined in two contexts. Two datasets are used: one containing data coming from an eye tracking ...
Bayesian modeling of visual attention
ICONIP'12: Proceedings of the 19th international conference on Neural Information Processing - Volume Part IIThe mechanism in the brain that determines which part of the multitude of sensory data is currently of most interest is called selective attention. There are two kinds of attention cues, stimulus-driven bottom-up cues and goal-driven top-down cues ...
Comments