ENCAPSULATION OF IMAGE METADATA FOR EASE OF RETRIEVAL AND MOBILITY
Nancy WOODS
chyn_woods@yahoo.comUniversity of Ibadan, Faculty of Science, Department of Computer Science, Oyo State, Ibadan (Nigeria)
Charles ROBERT
University of Ibadan, Faculty of Science, Department of Computer Science, Oyo State, Ibadan (Niger)
Abstract
Increasing proliferation of images due to multimedia capabilities of hand-held devices has resulted in loss of source information resulting from inherent mobility. These images are cumbersome to search out once stored away from their original source because they drop their descriptive data. This work, developed a model to encapsulate descriptive metadata into the Exif section of image header for effective retrieval and mobility. The resulting metadata used for retrieval purposes was mobile, searchable and non-obstructive.
Keywords:
automatic image annotation, image tagging, metadataReferences
mobile market statistics you should know in 2016. (2016, August 22). In Afilias Technologies Ltd. Retrieved from Device Atlas: https://deviceatlas.com/blog/16-mobile-market-statisticsyou-should-know-2016
Google Scholar
Ames, M., & Naaman, M. (2007). Why We Tag: Motivations for Annotation in Mobile and Online Media. CHI 2007, Tags, Tagging & Notetaking (pp. 971-980). California: ACM. https://doi.org/10.1145/1240624.1240772
DOI: https://doi.org/10.1145/1240624.1240772
Google Scholar
Chaffey, D. (2016). Global social media research summary 2016. Retrieved August 22, 2016, from Smart Insights: http://www.smartinsights.com/social-media-marketing/social-media-strategy/new-global-social-media-research/
Google Scholar
Duygulu, P., Barnard, K., Freitas, N., & Forsyth, D. (2002). Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary. The 7th European Conference on Computer Vision (pp. 97–112). Copenhagen. Extensible Metadata Platform (XMP). (2014, January 8). In Adobe Systems Incorporated. Retrieved from Adobe Systems Incorporated Web site: http://www.adobe.com/products/xmp.html
DOI: https://doi.org/10.1007/3-540-47979-1_7
Google Scholar
Feng, Y., & Lapata, M. (2008). Automatic Image Annotation Using Auxiliary Text Information. Association for Computational Linguistics -08 (pp. 272–280). Columbus: Association for Computational Linguistics.
Google Scholar
Gozali, J. P., Kan, M.-Y., & Sundaram, H. (2012). How do people organize their photos in each event and how does it affect storytelling, searching and interpretation tasks? Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries (pp. 315–324). Washington, DC: ACM New York. https://doi.org/10.1145/2232817.2232875
DOI: https://doi.org/10.1145/2232817.2232875
Google Scholar
Hanbury, A. (2008). A survey of methods for image annotation. Journal of Visual Languages & Computing, 19(5), 617–627. https://doi.org/10.1016/j.jvlc.2008.01.002
DOI: https://doi.org/10.1016/j.jvlc.2008.01.002
Google Scholar
Internet World Stats. Usage and population Statistics. (2016, August 22). In Internet World Stats. Retrieved from Internet World Stats: http://www.internetworldstats.com/stats1.htm
Google Scholar
IPTC Photo Metadata Standard. (2016, January 22). In International Press Telecommunications Council. Retrieved from International Press Telecommunications Council Website: https://iptc.org/standards/photo-metadata/iptc-standard
Google Scholar
Ivasic-Kos, M., Pobar, M., & Ribaric, S. (2016). Two-tier image annotation model based on a multilabel classifier and fuzzy-knowledge representation scheme. Pattern Recognition, 52, 287–305. https://doi.org/10.1016/j.patcog.2015.10.017
DOI: https://doi.org/10.1016/j.patcog.2015.10.017
Google Scholar
Jaimes, A. (2006). Human Factors in Automatic Image Retrieval System Design and Evaluation. Proceedings of SPIE Vol. #6061, Internet Imaging VII. San Jose, CA, USA. https://doi.org/10.1117/12.660255
DOI: https://doi.org/10.1117/12.660255
Google Scholar
Japan Electronics and Information Technology Industries Association. (2002). Exchangeable image file format for digital still cameras: Exif Version 2.3. Japan: Japan Electronics and Information Technology Industries Association.
Google Scholar
Jeon, J., Lavrenko, V., & Manmatha, R. (2003). Automatic Image Annotation and Retrieval using Cross-Media Relevance Models. SIGIR’03. Toronto: ACM. https://doi.org/10.1145/860435.860459
DOI: https://doi.org/10.1145/860435.860459
Google Scholar
Kuric, E., & Bielikovan, M. (2015). ANNOR: Efficient image annotation based on combining local and global features. Computers & Graphics, 47, 1–15. https://doi.org/10.1016/j.cag.2014.09.035
DOI: https://doi.org/10.1016/j.cag.2014.09.035
Google Scholar
Kustanowitz, J., & Shneiderman, B. (2005). Motivating Annotation for Personal Digital Photo Libraries: Lowering Barriers While Raising Incentives. Tech. Report HCIL–2004–18. University of Marlyand.
Google Scholar
Lavrenko, V., Manmatha, R., & Jeon, J. (2003). A model for learning the semantics of pictures. The 16th Conference on Advances in Neural Information Processing Systems. Vancouver.
Google Scholar
Makadia, A., Pavlovic, V., & Kumar, S. (2008). A New Baseline for Image Annotation. In T. P. Forsyth D. (Ed.), ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III (pp. 316–329). Berlin, Heidelberg: Springer. https://doi.org/0.1.1.145.9205
DOI: https://doi.org/10.1007/978-3-540-88690-7_24
Google Scholar
Monthly Subscriber Data. (2017, August 22). In The Nigerian Communications Commission. Retrieved from NCC Subscriber Statistics: http://ncc.gov.ng/index.php?option=com_content&view=article&id=125:subscriber-statistics&catid=65:industryinformation&Itemid=73
Google Scholar
Mori, Y., Takahashi, H., & Oka, R. (1999). Image-to-word transformation based on dividing and vector quantizing images with words. Proceedings of the 1st International Workshop on Multimedia Intelligent Storage and Retrieval Management. Orlando. https://doi.org/10.1.1.31.1704
Google Scholar
National Information Standards Organization. (2004). Understanding Metadata. Bethesda, USA: NISO Press.
Google Scholar
National Information Standards Organization. (2015). RLG Technical Metadata for Images Workshop Report. Retrieved from National Information Standards Organization: http://www.niso.org/imagerpt.html
Google Scholar
Numbers, Facts and Trends Shaping Your World. (2015, July 20). In Pew Research Centre. Retrieved from http://www.pewresearch.org
Google Scholar
Pew Global. (2016, August 22). In Pew Research Center. Retrieved from Pew Research Center, Global Attitudes & Trends: http://www.pewglobal.org/2015/04/15/cell-phones-in-africacommunication-lifeline
Google Scholar
Rodden, K., & Wood, K. R. (2003). How Do People Manage Their Digital Photographs? SIGCHI Conference on Human Factors in Computing Systems (pp. 409–416). Florida: ACM New York. https://doi.org/10.1145/642611.642682
DOI: https://doi.org/10.1145/642611.642682
Google Scholar
Smartphone Users Worldwide Will Total 1.75 Billion in 2014. (2015, July 20). In eMarketer. Retrieved from http://www.emarketer.com/Article/Smartphone-Users-Worldwide-WillTotal-175-Billion-2014/1010536
Google Scholar
Smith, A. (2015). US smartphone use in 2015. Retrieved July 20, 2015, from Pew Research Centre: http://www.pewinternet.org/2015/04/01/us-smartphone-use-in-2015/
Google Scholar
Strydom, T. (2015). Facebook rakes in users in Nigeria and Kenya, eyes rest of Africa. Retrieved August 22, 2016, from Reuters: http://www.reuters.com/article/us-facebook-africaidUSKCN0RA17L20150910
Google Scholar
Wang, X.-J., Zhang, L., Jing, F., & Ma, W.–Y. (2006). AnnoSearch: Image Auto-Annotation by Search. The 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE. doi:10.1109/CVPR.2006.58
DOI: https://doi.org/10.1109/CVPR.2006.58
Google Scholar
Wenyin, L., Dumais, S., Sun, Y., Zhang, H., Czerwinski, M., & Field, B. (2001). Semi-Automatic Image Annotation. INTERACT '01: IFIP TC13 International Conference on HumanComputer Interaction (pp. 326–333). IOS Press.
Google Scholar
Weston, J., Bengio, S., & Usunier, N. (2010). Large scale image annotation: learning to rank with joint word-image embeddings. Machine Learning, 81, 21–35. https://doi.org/10.1007/s10994-010-5198-3
DOI: https://doi.org/10.1007/s10994-010-5198-3
Google Scholar
Woods, N. C. (2017). Low-level Multimedia Recognition and Classification for Intelligence and Forensic Analysis. Unpublished Thesis.
Google Scholar
World Population Review. (2017, October 3). In World Population Review. Retrieved from http://worldpopulationreview.com/countries/nigeria-population
Google Scholar
Zhang, D., Islam, M. M., & Lu, G. (2012). A review on automatic image annotation techniques. Pattern Recognition, 45(1), 346–362. https://doi.org/10.1016/j.patcog.2011.05.013
DOI: https://doi.org/10.1016/j.patcog.2011.05.013
Google Scholar
Authors
Nancy WOODSchyn_woods@yahoo.com
University of Ibadan, Faculty of Science, Department of Computer Science, Oyo State, Ibadan Nigeria
Authors
Charles ROBERTUniversity of Ibadan, Faculty of Science, Department of Computer Science, Oyo State, Ibadan Niger
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