Describing and Assessing Image Descriptions for Visually Impaired Web Users with IDAT

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 179)


People with visual impairments, particularly blind people face alot of difficulties browsing the web with assistive technologies such as screen readers, when websites do not conform to accessibility standards and are thus inaccessible. HTML is the basic language for website design but its ALT attribute on the IMG element does not adequately capture comprehensive image semantics and description in a way that can be accurately interpreted by screen readers, hence blind people do not usually get the complete description of the image. Most of the problems however arise from web designers and developers not including a description of an image or not comprehensively describing these images to people with visual impairments. In this paper, we propose the use of the Image Description Assessment Tool (IDAT), a Java-based tool containing some proposed heuristics for assessing how well an image description matches the real content of the image on the web. The tool also contains a speech interface which can enable a visually impaired individual to listen to the description of an image that has been uploaded unto the system.


Resource Description Framework Disable People Assistive Technology Weighting Score Image Description 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors would like to thank the Department of Computer Science, University of Hull, UK for funding that enabled this research to be carried out and presented. Many thanks to the anonymous reviewers of this paper for their comments and to Shawulu H. Nggada for his insightful comments on refining and improving IDAT.


  1. 1.
    Petrie, H., Harrison, C., Dev, S.: Describing images on the web: a survey of current practice and prospects for the future. In: Universal Access in HCI: Exploring New Dimensions of Diversity, Vol. 8, Proceedings of the 3rd International Conference on Universal Access in Human-Computer Interaction, 22–27 July 2005, Las Vegas, Nevada). New Jersey: Lawrence Erlbaum Associates, (2005)Google Scholar
  2. 2.
    Baguma, R., Lubega, J.T.: Web design requirements for improved web accessibility for the blind. In: Fong, J., Kwan, R, Wang, F.L. (eds.) Hybrid Learning and Education. Lect. Notes in Comput. Sci., pp. 392–403. Springer, Heidelberg (2008)Google Scholar
  3. 3.
    Berners-Lee, T., Hendler, J. and Lassila, O.: The Semantic Web, Scientific American, pp. 35–43, (2001)Google Scholar
  4. 4.
    Gruber, T.R.: A translation approach to portable ontology specifications. Knowledge Acquisition 5(2), 199–220 (1993)CrossRefGoogle Scholar
  5. 5.
    Yu, W., Kuber, R., Murphy, E., Strain, P., McAllister, G.: A novel multimodal interface for improving visually impaired people’s web accessibility. Virtual Reality 9, 133–148 (2006)CrossRefGoogle Scholar
  6. 6.
    Shi, Y.: The accessibility of queensland visitor information centre’s websites. Tour. Manag. 27(2), 829–841 (2006)CrossRefGoogle Scholar
  7. 7.
    Chiang, M.F., Cole, R.G., Gupta, S., Kaiser, G.E., Starren, J.B.: Computer and world wide web accessibility by visually disabled patients: Problems and Solutions. Surv. of Ophthalmol., vol. 50, no.4, (2005)Google Scholar
  8. 8.
    Shatford, S.: Analyzing the subject of a picture: a theoretical approach. Cataloging & Classif. q. 6(3), 39–62 (1986)CrossRefGoogle Scholar
  9. 9.
    Panofsky, E.: Studies in Iconology: Humanistic themes in the art of the renaissance, pp. 5–9. Harper & Row, New York (1972)Google Scholar
  10. 10.
    Keysers, D., Renn, M., Breuel, T.M.: Improving accessibility of HTML documents by generating image-tags in a proxy. In: Proceedings of the Ninth International ACM SIGACCESS Conference on Computers and Accessibility, pp. 249–250, (2007)Google Scholar
  11. 11.
    Hollink, L., Schreiber, G., Wielinga, B., Worring, M.: Classification of user image descriptions. Int. J. Hum. Comput. Stud. 61(5), 501–626 (2004)CrossRefGoogle Scholar
  12. 12.
    Yang, H.C., Lee, C.H.: Image semantics discovery from web pages for semantic-based image retrieval using self-organizing maps. Expert Syst. with Appl. 34(1), 266–279 (2008)CrossRefGoogle Scholar
  13. 13.
    Elahi, N., Karlsen, R., Akselsen, S.: A context centric approach for semantic image annotation and retrieval, computationworld. Computation world: Future computing, service computation, cognitive, adaptive, content, patterns, pp. 66–668, (2009)Google Scholar
  14. 14.
    Lassila, O., Swick, R.: Resource description framework model and syntax specification. World Wide Web Consortium (1999). Accessed 10 Mar 2011
  15. 15.
    McGuinness, D.L., Van Harmelen, F.: Web Ontology Language Overview. World Wide Web Consortium (2004). Accessed 10 Mar 2011
  16. 16.
    Ruotsalo, T.: Methods and Applications for Ontology-Based Recommender Systems. Aalto University, Doctoral Diss (2010)Google Scholar
  17. 17.
    Castells, P., Fernández, M., Vallet, D.: An adaptation of the vector-space model for ontology-based information retrieval. IEEE Trans. Knowl. Data Eng. 19(2), 261–272 (2007)CrossRefGoogle Scholar
  18. 18.
    Ohler, J.: The Semantic web in education. Educause Q. 31(4), 7–9 (2008)Google Scholar
  19. 19.
    Lewiecki, E.M., Rudolph, L.: AKiebzak, G. M., Chavez, J. R., Thorpe, B. M. :Assessment of osteoporosis-website quality. Osteoporos Int 17, 741–752 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Distributed Reliable Intelligent Systems (DRIS) Lab, Department of Computer ScienceUniversity of HullHullUnited Kingdom
  2. 2.Department of Computer ScienceUniversity of HullHullUnited Kingdom

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