Measuring the Visual Complexities of Web Pages

Visual complexities (VisComs) of Web pages significantly affect user experience, and automatic evaluation can facilitate a large number of Web-based applications. The construction of a model for measuring the VisComs of Web pages requires the extraction of typical features and learning based on labeled Web pages. However, as far as the authors are aware, little headway has been made on measuring VisCom in Web mining and machine learning. The present article provides a new approach combining Web mining techniques and machine learning algorithms for measuring the VisComs of Web pages. The structure of a Web page is first analyzed, and the layout is then extracted. Using a Web page as a semistructured image, three classes of features are extracted to construct a feature vector. The feature vector is fed into a learned measuring function to calculate the VisCom of the page. In the proposed approach of the present study, the type of the measuring function and its learning depend on the quantification strategy for VisCom. Aside from using a category and a score to represent VisCom as existing work, this study presents a new strategy utilizing a distribution to quantify the VisCom of a Web page. Empirical evaluation suggests the effectiveness of the proposed approach in terms of both features and learning algorithms.

[1]  José San Pedro,et al.  Ranking and classifying attractiveness of photos in folksonomies , 2009, WWW '09.

[2]  Thomas Hofmann,et al.  Support vector machine learning for interdependent and structured output spaces , 2004, ICML.

[3]  Thomas Wilhelm,et al.  What is a complex graph , 2008 .

[4]  Guangyu Chen,et al.  Web page genre classification , 2008, SAC '08.

[5]  Lijuan Cao,et al.  A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine , 2003, Neurocomputing.

[6]  Guangfeng Song.  Analysis of Web Page Complexity Through Visual Segmentation , 2007, HCI.

[7]  Yunfei Chen,et al.  Evaluating the visual quality of web pages using a computational aesthetic approach , 2011, WSDM '11.

[8]  J. Annett.  Subjective rating scales: science or art? , 2002, Ergonomics.

[9]  Thomas S. Tullis.  A method for evaluating Web page design concepts , 1998, CHI Conference Summary.

[10]  Sean Bechhofer,et al.  Visual complexity and aesthetic perception of web pages , 2008, SIGDOC '08.

[11]  Robert Stevens,et al.  Toward a definition of visual complexity as an implicit measure of cognitive load , 2009, TAP.

[12]  Ishani Chakraborty,et al.  Correlating low-level image statistics with users - rapid aesthetic and affective judgments of web pages , 2009, CHI.

[13]  Eleni Michailidou,et al.  ViCRAM: visual complexity rankings and accessibility metrics , 2006, ASAC.

[14]  Moni Naor,et al.  Rank aggregation methods for the Web , 2001, WWW '01.

[15]  Nikolaos M. Avouris,et al.  Bayesian Modelling of Colour's Usage Impact to Web Credibility , 2006, ECAI.

[16]  Bing Liu,et al.  Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data , 2006, Data-Centric Systems and Applications.

[17]  Paul van Schaik,et al.  The effects of screen ratio and order on information retrieval in web pages , 2003 .

[18]  Erick Cantú-Paz,et al.  Personalized click prediction in sponsored search , 2010, WSDM '10.

[19]  Jian Pei,et al.  Web search/browse log mining: challenges, methods, and applications , 2010, WWW '10.

[20]  Wei-Ying Ma,et al.  Extracting Content Structure for Web Pages Based on Visual Representation , 2003, APWeb.

[21]  Earl A. Alluisi,et al.  Studies in the New Experimental Aesthetics , 1975 .

[22]  Patrick Le Callet,et al.  Considering Temporal Variations of Spatial Visual Distortions in Video Quality Assessment , 2009, IEEE Journal of Selected Topics in Signal Processing.

[23]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[24]  James Ze Wang,et al.  Studying Aesthetics in Photographic Images Using a Computational Approach , 2006, ECCV.

[25]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[26]  Alexandre N. Tuch,et al.  The Role of Visual Complexity in Affective Reactions to Webpages: Subjective, Eye Movement, and Cardiovascular Responses , 2011, IEEE Transactions on Affective Computing.

[27]  Wolfgang Nejdl,et al.  A densitometric approach to web page segmentation , 2008, CIKM '08.

[28]  Michel Wedel,et al.  The Stopping Power of Advertising: Measures and Effects of Visual Complexity , 2010 .

[29]  Arnold W. M. Smeulders,et al.  c ○ 2005 Springer Science + Business Media, Inc. Manufactured in The Netherlands. A Six-Stimulus Theory for Stochastic Texture , 2002 .

[30]  Ani Nenkova,et al.  Revisiting Readability: A Unified Framework for Predicting Text Quality , 2008, EMNLP.

[31]  Sabine Süsstrunk,et al.  Measuring colorfulness in natural images , 2003, IS&T/SPIE Electronic Imaging.

[32]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[33]  J. Fleiss.  Measuring nominal scale agreement among many raters. , 1971 .

[34]  Wei-Ying Ma,et al.  VIPS: a Vision-based Page Segmentation Algorithm , 2003 .

[35]  ShiLei,et al.  Measuring the Visual Complexities of Web Pages , 2013 .

[36]  Otman A. Basir,et al.  An Intelligent Expert Systems' Approach to Layout Decision Analysis and Design under Uncertainty , 2008, Intelligent Decision Making: An AI-Based Approach.

[37]  M. Longnecker,et al.  A modified Wilcoxon rank sum test for paired data , 1983 .

[38]  D. Donderi.  Visual complexity: a review. , 2006, Psychological bulletin.

[39]  Ishani Chakraborty,et al.  Developing Quantitative Metrics to Predict Users' Perceptions of Interface Design , 2008 .

[40]  John Knight,et al.  Homepage aesthetics: The search for preference factors and the challenges of subjectivity , 2006, Interact. Comput..

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

[42]  Martin Ebner,et al.  The XAOS Metric - Understanding Visual Complexity as Measure of Usability , 2010, USAB.

[43]  Eleni Michailidou.  Visual complexity rankings and accessibility metrics , 2010 .

[44]  Nikolaos Avouris,et al.  Bayesian modelling of impact of colour on web credibility , 2006 .

[45]  David A. Landgrebe,et al.  A survey of decision tree classifier methodology , 1991, IEEE Trans. Syst. Man Cybern..

[46]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[47]  Jinwoo Kim,et al.  Critical factors for the aesthetic fidelity of web pages: empirical studies with professional web designers and users , 2004, Interact. Comput..

[48]  Meng Wang,et al.  Active learning in multimedia annotation and retrieval: A survey , 2011, TIST.

[49]  Sören Sonnenburg,et al.  Optimized cutting plane algorithm for support vector machines , 2008, ICML '08.

[50]  R. Watson,et al.  The Influence of Home Page Complexity on Consumer Attention, Attitudes, and Purchase Intent , 2006 .

[51]  Michal Cutler,et al.  Cost-Sensitive Feature Extraction and Selection in Genre Classification , 2009, J. Lang. Technol. Comput. Linguistics.

[52]  Alexandra Forsythe,et al.  Visual Complexity: Is That All There Is? , 2009, HCI.

[53]  Noel Sheehy,et al.  Measuring icon complexity: An automated analysis , 2003, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.

[54]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[55]  Alexandre N. Tuch,et al.  Visual complexity of websites: Effects on users' experience, physiology, performance, and memory , 2009, Int. J. Hum. Comput. Stud..

[56]  Michael D. Buhrmester,et al.  Amazon's Mechanical Turk , 2011, Perspectives on psychological science : a journal of the Association for Psychological Science.

[57]  Yuanzhen Li,et al.  Measuring visual clutter. , 2007, Journal of vision.

[58]  Wei-Ying Ma,et al.  Learning block importance models for web pages , 2004, WWW '04.