Advertisement

Universal Access in the Information Society

, Volume 17, Issue 3, pp 607–619 | Cite as

Accessible images (AIMS): a model to build self-describing images for assisting screen reader users

  • Ab Shaqoor Nengroo
  • K. S. Kuppusamy
Long Paper

Abstract

Non-visual web access depends on the textual description of various non-text elements of web pages. The existing methods of describing images for non-visual access do not provide a strong coupling between described images and their description. If an image is reused multiple times either in a single Web site or across multiple times, it is required to keep the description at all instances. This paper presents a tightly coupled model termed accessible images (AIMS) which utilizes a steganography-based approach to embed the description in the images at the server side and updating alt text of the web pages with the description extracted with the help of a browser extension. The proposed AIMS model has been built, targeting toward a web image description ecosystem in which images evolve into a self-description phase. The primary advantage of the proposed AIMS model is the elimination of the redundant description of an image resource at multiple instances. The experiments conducted on a dataset confirm that the AIMS model is capable of embedding and extracting descriptions with an accuracy level of 99.6%.

Keywords

Image accessibility Alternative text Steganography Least significant bit (LSB) Browser extension 

References

  1. 1.
    Amin, P., Subbalakshmi, K.P.: Rotation and cropping resilient data hiding with Zernike moments. In: 2004 International Conference on Image Processing, 2004. ICIP’04, vol. 4, pp. 2175–2178. IEEE (2004)Google Scholar
  2. 2.
    Bigham, J.P., Jayant, C., Ji, H., Little, G., Miller, A., Miller, R.C., Miller, R., Tatarowicz, A., White, B., White, S., et al.: Vizwiz: nearly real-time answers to visual questions. In: Proceedings of the 23nd Annual ACM Symposium on User Interface Software and Technology, pp. 333–342. ACM (2010)Google Scholar
  3. 3.
    Bigham, J.P., Kaminsky, R.S., Ladner, R.E., Danielsson, O.M., Hempton, G.L.: Webinsight:: making web images accessible. In: Proceedings of the 8th International ACM SIGACCESS Conference on Computers and Accessibility, pp. 181–188. ACM (2006)Google Scholar
  4. 4.
    Brady, E.L., Zhong, Y., Morris, M.R., Bigham, J.P.: Investigating the appropriateness of social network question asking as a resource for blind users. In: Proceedings of the 2013 Conference on Computer Supported Cooperative Work, pp. 1225–1236. ACM (2013)Google Scholar
  5. 5.
    Condron, M.: Managing the Digital You: Where and How to Keep and Organize Your Digital Life, 1st edn. Rowman & Littlefield Publishers, Lanham (2017)Google Scholar
  6. 6.
    Eggert, E., Abou-Zahra, S.: Images concepts. https://www.w3.org/WAI/tutorials/images/tips/ (2015)
  7. 7.
    Fang, H., Gupta, S., Iandola, F., Srivastava, R.K., Deng, L., Dollár, P., Gao, J., He, X., Mitchell, M., Platt, J.C., et al.: From captions to visual concepts and back. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1473–1482 (2015)Google Scholar
  8. 8.
    Freedom Scientific: The world’s most popular windows screen reader. http://www.freedomscientific.com/Products/Blindness/JAWS (2016)
  9. 9.
    Goljan, M., Fridrich, J.J., Du, R.: Distortion-free data embedding for images. In: International Workshop on Information Hiding, pp. 27–41. Springer (2001)Google Scholar
  10. 10.
    Goodwin, M., Susar, D., Nietzio, A., Snaprud, M., Jensen, C.S.: Global web accessibility analysis of national government portals and ministry web sites. J. Inf. Technol. Politics 8(1), 41–67 (2011)CrossRefGoogle Scholar
  11. 11.
    Guo, A., Chen, X., Qi, H., White, S., Ghosh, S., Asakawa, C., Bigham, J.P.: Vizlens: A robust and interactive screen reader for interfaces in the real world. In: Proceedings of the 29th Annual Symposium on User Interface Software and Technology, pp. 651–664. ACM (2016)Google Scholar
  12. 12.
    Hamid, N., Yahya, A., Ahmad, R.B., Al-Qershi, O.M.: Image steganography techniques: an overview. Int. J. Comput. Sci. Secur. 6(3), 168–187 (2012)Google Scholar
  13. 13.
    Henry, S.L., McGee., L.: Accessibility. https://www.w3.org/standards/webdesign/accessibility (2016)
  14. 14.
    Jaro, M.A.: Advances in record-linkage methodology as applied to matching the 1985 census of Tampa, Florida. J. Am. Stat. Assoc. 84(406), 414–420 (1989)CrossRefGoogle Scholar
  15. 15.
    Karpathy, A., Fei-Fei, L.: Deep visual-semantic alignments for generating image descriptions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3128–3137 (2015)Google Scholar
  16. 16.
    Krause, J., Johnson, J., Krishna, R., Fei-Fei, L.: A hierarchical approach for generating descriptive image paragraphs (2016). arXiv preprint arXiv:1611.06607
  17. 17.
    Li, B., He, J., Huang, J., Shi, Y.Q.: A survey on image steganography and steganalysis. J. Inf. Hiding Multimed. Signal Process. 2(2), 142–172 (2011)Google Scholar
  18. 18.
    Lin, E.T., Delp, E.J.: A review of data hiding in digital images. In: IS and TS PICS Conference, pp. 274–278. Society for Imaging Science & Technology (1999)Google Scholar
  19. 19.
    Michael Curran, J.T.: Empowering lives through non-visual access to technology. https://www.nvaccess.org/ (2017)
  20. 20.
    Morkel, T., Eloff, J.H., Olivier, M.S.: An overview of image steganography. In: ISSA, pp. 1–11 (2005)Google Scholar
  21. 21.
    Park, E., Lim, H.: A study on improvement of evaluation method on web accessibility automatic evaluation tool’s <img> alternative texts based on OCR (2015)Google Scholar
  22. 22.
    Park, E., Lim, H.: A study on providing alternative text of image for web accessibility improvement. Int. J. Appl. Eng. Res. 11(2), 762–765 (2016)Google Scholar
  23. 23.
    Parulski, K.A., McCoy, J.R.: Method for adding personalized metadata to a collection of digital images. US Patent 6629104, 2003Google Scholar
  24. 24.
    Parulski, K.A., McCoy, J.R.: Digital camera for capturing images and selecting metadata to be associated with the captured images. US Patent 7171113, 2007Google Scholar
  25. 25.
    Pennsylvania State University: Image alt tag tips for html. http://accessibility.psu.edu/images/imageshtml/ (2016)
  26. 26.
    Petrie, H., Harrison, C., Dev, S.: Describing images on the web: a survey of current practice and prospects for the future. In: Proceedings of Human Computer Interaction International (HCII), vol. 71 (2005)Google Scholar
  27. 27.
    Potdar, V.M., Han, S., Chang, E.: Fingerprinted secret sharing steganography for robustness against image cropping attacks. In: 2005 3rd IEEE International Conference on Industrial Informatics, 2005. INDIN’05, pp. 717–724. IEEE (2005)Google Scholar
  28. 28.
    Pradhan, A., Sahu, A.K., Swain, G., Sekhar, K.R.: Performance evaluation parameters of image steganography techniques. In: International Conference on Research Advances in Integrated Navigation Systems (RAINS), pp. 1–8. IEEE (2016)Google Scholar
  29. 29.
    Queirolo, F.: Steganography in images. Final Communications Report, vol. 3. http://eric.purpletree.org/file/Steganography%20In%20Images.pdf (2011)
  30. 30.
    Rana, M.: Parameter evaluation and comparison of algorithms used in steganography. Int. J. Eng. Sci. Comput.  https://doi.org/10.4010/2016.1901 (2016)
  31. 31.
    Shin, H., Lim, J., Park, J.: Information visualization and information presentation for visually impaired people. ETRI J. 28(1), 81–91 (2013)Google Scholar
  32. 32.
    Steve, F.: Html5: Techniques for providing useful text alternatives. https://www.w3.org/TR/2011/WD-html-alt-techniques-20110113/ (2017)
  33. 33.
    Tran, K., He, X., Zhang, L., Sun, J., Carapcea, C., Thrasher, C., Buehler, C., Sienkiewicz, C.: Rich image captioning in the wild (2016). arXiv preprint arXiv:1603.09016
  34. 34.
    Vinyals, O., Toshev, A., Bengio, S., Erhan, D.: Show and tell: lessons learned from the 2015 MSCOCO image captioning challenge. IEEE Trans. Pattern Anal. Mach. Intell. 39, 652–663 (2016)CrossRefGoogle Scholar
  35. 35.
    Von Ahn, L., Dabbish, L.: Labeling images with a computer game. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 319–326. ACM (2004)Google Scholar
  36. 36.
    Von Ahn, L., Ginosar, S., Kedia, M., Liu, R., Blum, M.: Improving accessibility of the web with a computer game. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 79–82. ACM (2006)Google Scholar
  37. 37.
    W3C Working Group on Cascading Style Sheets: Media queries level 4-w3c working draft. https://www.w3.org/TR/mediaqueries-4/ (2016)
  38. 38.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)CrossRefGoogle Scholar
  39. 39.
    WHO: “visual impairment and blindness”, fact sheet 2014. http://www.who.int/mediacentre/factsheets/fs282/en/ (2014)
  40. 40.
    Winkler, W.E.: String comparator metrics and enhanced decision rules in the Fellegi–Sunter model of record linkage (1990)Google Scholar
  41. 41.
    Wu, Q., Shen, C., Hengel, A.v.d., Wang, P., Dick, A.: Image captioning and visual question answering based on attributes and their related external knowledge (2016). arXiv preprint arXiv:1603.02814

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Department of Computer SciencePondicherry UniversityPondicherryIndia

Personalised recommendations